idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
|---|---|---|---|---|---|---|
401 | Famous statistical quotations | The combination of some data and an
aching desire for an answer does not
ensure that a reasonable answer can be
extracted from a given body of data
Tukey | Famous statistical quotations | The combination of some data and an
aching desire for an answer does not
ensure that a reasonable answer can be
extracted from a given body of data
Tukey | Famous statistical quotations
The combination of some data and an
aching desire for an answer does not
ensure that a reasonable answer can be
extracted from a given body of data
Tukey | Famous statistical quotations
The combination of some data and an
aching desire for an answer does not
ensure that a reasonable answer can be
extracted from a given body of data
Tukey |
402 | Famous statistical quotations | There are no routine statistical
questions, only questionable
statistical routines.
D.R. Cox | Famous statistical quotations | There are no routine statistical
questions, only questionable
statistical routines.
D.R. Cox | Famous statistical quotations
There are no routine statistical
questions, only questionable
statistical routines.
D.R. Cox | Famous statistical quotations
There are no routine statistical
questions, only questionable
statistical routines.
D.R. Cox |
403 | Famous statistical quotations | Statistics - A subject which most statisticians find difficult but which many physicians are experts on. "Stephen S. Senn" | Famous statistical quotations | Statistics - A subject which most statisticians find difficult but which many physicians are experts on. "Stephen S. Senn" | Famous statistical quotations
Statistics - A subject which most statisticians find difficult but which many physicians are experts on. "Stephen S. Senn" | Famous statistical quotations
Statistics - A subject which most statisticians find difficult but which many physicians are experts on. "Stephen S. Senn" |
404 | Famous statistical quotations | He uses statistics like a drunken man uses a lamp post, more for support than illumination.
-- Andrew Lang | Famous statistical quotations | He uses statistics like a drunken man uses a lamp post, more for support than illumination.
-- Andrew Lang | Famous statistical quotations
He uses statistics like a drunken man uses a lamp post, more for support than illumination.
-- Andrew Lang | Famous statistical quotations
He uses statistics like a drunken man uses a lamp post, more for support than illumination.
-- Andrew Lang |
405 | Famous statistical quotations | Strange events permit themselves the
luxury of occurring.
-- Charlie Chan | Famous statistical quotations | Strange events permit themselves the
luxury of occurring.
-- Charlie Chan | Famous statistical quotations
Strange events permit themselves the
luxury of occurring.
-- Charlie Chan | Famous statistical quotations
Strange events permit themselves the
luxury of occurring.
-- Charlie Chan |
406 | Famous statistical quotations | A nice one I came about:
I think it's much more interesting to live not knowing than to have answers which might be wrong.
By Richard Feynman (link) | Famous statistical quotations | A nice one I came about:
I think it's much more interesting to live not knowing than to have answers which might be wrong.
By Richard Feynman (link) | Famous statistical quotations
A nice one I came about:
I think it's much more interesting to live not knowing than to have answers which might be wrong.
By Richard Feynman (link) | Famous statistical quotations
A nice one I came about:
I think it's much more interesting to live not knowing than to have answers which might be wrong.
By Richard Feynman (link) |
407 | Famous statistical quotations | The best thing about being a statistician is that you get to play in everyone's backyard.
-- John Tukey
(This is MY favourite Tukey quote) | Famous statistical quotations | The best thing about being a statistician is that you get to play in everyone's backyard.
-- John Tukey
(This is MY favourite Tukey quote) | Famous statistical quotations
The best thing about being a statistician is that you get to play in everyone's backyard.
-- John Tukey
(This is MY favourite Tukey quote) | Famous statistical quotations
The best thing about being a statistician is that you get to play in everyone's backyard.
-- John Tukey
(This is MY favourite Tukey quote) |
408 | Famous statistical quotations | Absence of evidence is not evidence of absence.
–Martin Rees (Wikipedia) | Famous statistical quotations | Absence of evidence is not evidence of absence.
–Martin Rees (Wikipedia) | Famous statistical quotations
Absence of evidence is not evidence of absence.
–Martin Rees (Wikipedia) | Famous statistical quotations
Absence of evidence is not evidence of absence.
–Martin Rees (Wikipedia) |
409 | Famous statistical quotations | "It's easy to lie with statistics; it is easier to lie without them."
-- Frederick Mosteller | Famous statistical quotations | "It's easy to lie with statistics; it is easier to lie without them."
-- Frederick Mosteller | Famous statistical quotations
"It's easy to lie with statistics; it is easier to lie without them."
-- Frederick Mosteller | Famous statistical quotations
"It's easy to lie with statistics; it is easier to lie without them."
-- Frederick Mosteller |
410 | Famous statistical quotations | Say you were standing with one foot in the oven and one foot in an ice bucket. According to the percentage people, you should be perfectly comfortable.
-Bobby Bragan, 1963 | Famous statistical quotations | Say you were standing with one foot in the oven and one foot in an ice bucket. According to the percentage people, you should be perfectly comfortable.
-Bobby Bragan, 1963 | Famous statistical quotations
Say you were standing with one foot in the oven and one foot in an ice bucket. According to the percentage people, you should be perfectly comfortable.
-Bobby Bragan, 1963 | Famous statistical quotations
Say you were standing with one foot in the oven and one foot in an ice bucket. According to the percentage people, you should be perfectly comfortable.
-Bobby Bragan, 1963 |
411 | Famous statistical quotations | Tout le monde y croit cependant, me disait un jour M. Lippmann, car les expérimentateurs s'imaginent que c'est un théorème de mathématiques, et les mathématiciens que c'est un fait expérimental.
Henri Poincaré, Calcul des probabilités (2nd ed., 1912), p. 171.
In English:
Everybody believes in the exponential law of e... | Famous statistical quotations | Tout le monde y croit cependant, me disait un jour M. Lippmann, car les expérimentateurs s'imaginent que c'est un théorème de mathématiques, et les mathématiciens que c'est un fait expérimental.
Henr | Famous statistical quotations
Tout le monde y croit cependant, me disait un jour M. Lippmann, car les expérimentateurs s'imaginent que c'est un théorème de mathématiques, et les mathématiciens que c'est un fait expérimental.
Henri Poincaré, Calcul des probabilités (2nd ed., 1912), p. 171.
In English:
Everybody believ... | Famous statistical quotations
Tout le monde y croit cependant, me disait un jour M. Lippmann, car les expérimentateurs s'imaginent que c'est un théorème de mathématiques, et les mathématiciens que c'est un fait expérimental.
Henr |
412 | Famous statistical quotations | My greatest concern was what to call
it. I thought of calling it
'information,' but the word was overly
used, so I decided to call it
'uncertainty.' When I discussed it
with John von Neumann, he had a better
idea. Von Neumann told me, 'You should
call it entropy, for two reasons. In
the first place your... | Famous statistical quotations | My greatest concern was what to call
it. I thought of calling it
'information,' but the word was overly
used, so I decided to call it
'uncertainty.' When I discussed it
with John von Neumann | Famous statistical quotations
My greatest concern was what to call
it. I thought of calling it
'information,' but the word was overly
used, so I decided to call it
'uncertainty.' When I discussed it
with John von Neumann, he had a better
idea. Von Neumann told me, 'You should
call it entropy, for two reas... | Famous statistical quotations
My greatest concern was what to call
it. I thought of calling it
'information,' but the word was overly
used, so I decided to call it
'uncertainty.' When I discussed it
with John von Neumann |
413 | Famous statistical quotations | I don't know about famous, but the following is one of my favourites:
Conducting data analysis is like
drinking a fine wine. It is important
to swirl and sniff the wine, to unpack
the complex bouquet and to appreciate
the experience. Gulping the wine
doesn’t work.
-Daniel B. Wright (2003), see PDF of Articl... | Famous statistical quotations | I don't know about famous, but the following is one of my favourites:
Conducting data analysis is like
drinking a fine wine. It is important
to swirl and sniff the wine, to unpack
the complex b | Famous statistical quotations
I don't know about famous, but the following is one of my favourites:
Conducting data analysis is like
drinking a fine wine. It is important
to swirl and sniff the wine, to unpack
the complex bouquet and to appreciate
the experience. Gulping the wine
doesn’t work.
-Daniel B. Wr... | Famous statistical quotations
I don't know about famous, but the following is one of my favourites:
Conducting data analysis is like
drinking a fine wine. It is important
to swirl and sniff the wine, to unpack
the complex b |
414 | Famous statistical quotations | ... surely, God loves the .06 nearly as much as the .05. Can there be any
doubt that God views the strength of evidence for or against the null as a
fairly continuous function of the magnitude of p? (p.1277)
Rosnow, R. L., & Rosenthal, R. (1989). Statistical procedures and the justification of knowledge in psychologic... | Famous statistical quotations | ... surely, God loves the .06 nearly as much as the .05. Can there be any
doubt that God views the strength of evidence for or against the null as a
fairly continuous function of the magnitude of p? ( | Famous statistical quotations
... surely, God loves the .06 nearly as much as the .05. Can there be any
doubt that God views the strength of evidence for or against the null as a
fairly continuous function of the magnitude of p? (p.1277)
Rosnow, R. L., & Rosenthal, R. (1989). Statistical procedures and the justificati... | Famous statistical quotations
... surely, God loves the .06 nearly as much as the .05. Can there be any
doubt that God views the strength of evidence for or against the null as a
fairly continuous function of the magnitude of p? ( |
415 | Famous statistical quotations | All we know about the world teaches us that the effects of A and B are always different---in some decimal place---for any A and B. Thus asking "are the effects different?" is foolish.
Tukey (again but this one is my favorite) | Famous statistical quotations | All we know about the world teaches us that the effects of A and B are always different---in some decimal place---for any A and B. Thus asking "are the effects different?" is foolish.
Tukey (again bu | Famous statistical quotations
All we know about the world teaches us that the effects of A and B are always different---in some decimal place---for any A and B. Thus asking "are the effects different?" is foolish.
Tukey (again but this one is my favorite) | Famous statistical quotations
All we know about the world teaches us that the effects of A and B are always different---in some decimal place---for any A and B. Thus asking "are the effects different?" is foolish.
Tukey (again bu |
416 | Famous statistical quotations | On two occasions I have been asked [by
members of Parliament], ‘Pray, Mr.
Babbage, if you put into the machine
wrong figures, will the right answers
come out?’ I am not able rightly to
apprehend the kind of confusion of
ideas that could provoke such a
question.
Charles Babbage | Famous statistical quotations | On two occasions I have been asked [by
members of Parliament], ‘Pray, Mr.
Babbage, if you put into the machine
wrong figures, will the right answers
come out?’ I am not able rightly to
appre | Famous statistical quotations
On two occasions I have been asked [by
members of Parliament], ‘Pray, Mr.
Babbage, if you put into the machine
wrong figures, will the right answers
come out?’ I am not able rightly to
apprehend the kind of confusion of
ideas that could provoke such a
question.
Charles Babba... | Famous statistical quotations
On two occasions I have been asked [by
members of Parliament], ‘Pray, Mr.
Babbage, if you put into the machine
wrong figures, will the right answers
come out?’ I am not able rightly to
appre |
417 | Famous statistical quotations | The subjectivist (i.e. Bayesian)
states his judgements, whereas the
objectivist sweeps them under the
carpet by calling assumptions
knowledge, and he basks in the
glorious objectivity of science.
I.J. Good | Famous statistical quotations | The subjectivist (i.e. Bayesian)
states his judgements, whereas the
objectivist sweeps them under the
carpet by calling assumptions
knowledge, and he basks in the
glorious objectivity of sci | Famous statistical quotations
The subjectivist (i.e. Bayesian)
states his judgements, whereas the
objectivist sweeps them under the
carpet by calling assumptions
knowledge, and he basks in the
glorious objectivity of science.
I.J. Good | Famous statistical quotations
The subjectivist (i.e. Bayesian)
states his judgements, whereas the
objectivist sweeps them under the
carpet by calling assumptions
knowledge, and he basks in the
glorious objectivity of sci |
418 | Famous statistical quotations | Do not trust any statistics you did not fake yourself.
-- Winston Churchill | Famous statistical quotations | Do not trust any statistics you did not fake yourself.
-- Winston Churchill | Famous statistical quotations
Do not trust any statistics you did not fake yourself.
-- Winston Churchill | Famous statistical quotations
Do not trust any statistics you did not fake yourself.
-- Winston Churchill |
419 | Is $R^2$ useful or dangerous? | To address the first question, consider the model
$$Y = X + \sin(X) + \varepsilon$$
with iid $\varepsilon$ of mean zero and finite variance. As the range of $X$ (thought of as fixed or random) increases, $R^2$ goes to 1. Nevertheless, if the variance of $\varepsilon$ is small (around 1 or less), the data are "noticea... | Is $R^2$ useful or dangerous? | To address the first question, consider the model
$$Y = X + \sin(X) + \varepsilon$$
with iid $\varepsilon$ of mean zero and finite variance. As the range of $X$ (thought of as fixed or random) increa | Is $R^2$ useful or dangerous?
To address the first question, consider the model
$$Y = X + \sin(X) + \varepsilon$$
with iid $\varepsilon$ of mean zero and finite variance. As the range of $X$ (thought of as fixed or random) increases, $R^2$ goes to 1. Nevertheless, if the variance of $\varepsilon$ is small (around 1 o... | Is $R^2$ useful or dangerous?
To address the first question, consider the model
$$Y = X + \sin(X) + \varepsilon$$
with iid $\varepsilon$ of mean zero and finite variance. As the range of $X$ (thought of as fixed or random) increa |
420 | Is $R^2$ useful or dangerous? | Your example only applies when the variable $\newcommand{\Var}{\mathrm{Var}}X$ should be in the model. It certainly doesn't apply when one uses the usual least squares estimates. To see this, note that if we estimate $a$ by least squares in your example, we get:
$$\hat{a}=\frac{\frac{1}{N}\sum_{i=1}^{N}X_{i}Y_{i}}{\f... | Is $R^2$ useful or dangerous? | Your example only applies when the variable $\newcommand{\Var}{\mathrm{Var}}X$ should be in the model. It certainly doesn't apply when one uses the usual least squares estimates. To see this, note t | Is $R^2$ useful or dangerous?
Your example only applies when the variable $\newcommand{\Var}{\mathrm{Var}}X$ should be in the model. It certainly doesn't apply when one uses the usual least squares estimates. To see this, note that if we estimate $a$ by least squares in your example, we get:
$$\hat{a}=\frac{\frac{1}{... | Is $R^2$ useful or dangerous?
Your example only applies when the variable $\newcommand{\Var}{\mathrm{Var}}X$ should be in the model. It certainly doesn't apply when one uses the usual least squares estimates. To see this, note t |
421 | Is $R^2$ useful or dangerous? | If I can add an example of when $R^2$ is dangerous. Many years ago I was working on some biometric data and being young and foolish I was delighted when I found some statistically significant $R^2$ values for my fancy regressions which I had constructed using stepwise functions. It was only afterwards looking back af... | Is $R^2$ useful or dangerous? | If I can add an example of when $R^2$ is dangerous. Many years ago I was working on some biometric data and being young and foolish I was delighted when I found some statistically significant $R^2$ v | Is $R^2$ useful or dangerous?
If I can add an example of when $R^2$ is dangerous. Many years ago I was working on some biometric data and being young and foolish I was delighted when I found some statistically significant $R^2$ values for my fancy regressions which I had constructed using stepwise functions. It was o... | Is $R^2$ useful or dangerous?
If I can add an example of when $R^2$ is dangerous. Many years ago I was working on some biometric data and being young and foolish I was delighted when I found some statistically significant $R^2$ v |
422 | Is $R^2$ useful or dangerous? | When you have a single predictor $R^{2}$ is exactly interpreted as the proportion of variation in $Y$ that can be explained by the linear relationship with $X$. This interpretation must be kept in mind when looking at the value of $R^2$.
You can get a large $R^2$ from a non-linear relationship only when the relationsh... | Is $R^2$ useful or dangerous? | When you have a single predictor $R^{2}$ is exactly interpreted as the proportion of variation in $Y$ that can be explained by the linear relationship with $X$. This interpretation must be kept in min | Is $R^2$ useful or dangerous?
When you have a single predictor $R^{2}$ is exactly interpreted as the proportion of variation in $Y$ that can be explained by the linear relationship with $X$. This interpretation must be kept in mind when looking at the value of $R^2$.
You can get a large $R^2$ from a non-linear relatio... | Is $R^2$ useful or dangerous?
When you have a single predictor $R^{2}$ is exactly interpreted as the proportion of variation in $Y$ that can be explained by the linear relationship with $X$. This interpretation must be kept in min |
423 | Is $R^2$ useful or dangerous? | One situation you would want to avoid $R^2$ is multiple regression, where adding irrelevant predictor variables to the model can in some cases increase $R^2$. This can be addressed by using the adjusted $R^2$ value instead, calculated as
$\bar{R}^2 = 1 - (1-R^2)\frac{n-1}{n-p-1}$ where $n$ is the number of data samples... | Is $R^2$ useful or dangerous? | One situation you would want to avoid $R^2$ is multiple regression, where adding irrelevant predictor variables to the model can in some cases increase $R^2$. This can be addressed by using the adjust | Is $R^2$ useful or dangerous?
One situation you would want to avoid $R^2$ is multiple regression, where adding irrelevant predictor variables to the model can in some cases increase $R^2$. This can be addressed by using the adjusted $R^2$ value instead, calculated as
$\bar{R}^2 = 1 - (1-R^2)\frac{n-1}{n-p-1}$ where $n$... | Is $R^2$ useful or dangerous?
One situation you would want to avoid $R^2$ is multiple regression, where adding irrelevant predictor variables to the model can in some cases increase $R^2$. This can be addressed by using the adjust |
424 | Is $R^2$ useful or dangerous? | A good example for high $R^2$ with a nonlinear function is the quadratic function $y=x^2$ restricted to the interval $[0,1]$. With 0 noise it will not have an $R^2$ square of 1 if you have 3 or more points since they will not fit perfectly on a straight line. But if the design points are scattered uniformly on the $[0... | Is $R^2$ useful or dangerous? | A good example for high $R^2$ with a nonlinear function is the quadratic function $y=x^2$ restricted to the interval $[0,1]$. With 0 noise it will not have an $R^2$ square of 1 if you have 3 or more p | Is $R^2$ useful or dangerous?
A good example for high $R^2$ with a nonlinear function is the quadratic function $y=x^2$ restricted to the interval $[0,1]$. With 0 noise it will not have an $R^2$ square of 1 if you have 3 or more points since they will not fit perfectly on a straight line. But if the design points are ... | Is $R^2$ useful or dangerous?
A good example for high $R^2$ with a nonlinear function is the quadratic function $y=x^2$ restricted to the interval $[0,1]$. With 0 noise it will not have an $R^2$ square of 1 if you have 3 or more p |
425 | How to choose a predictive model after k-fold cross-validation? | I think that you are missing something still in your understanding of the purpose of cross-validation.
Let's get some terminology straight, generally when we say 'a model' we refer to a particular method for describing how some input data relates to what we are trying to predict. We don't generally refer to particular ... | How to choose a predictive model after k-fold cross-validation? | I think that you are missing something still in your understanding of the purpose of cross-validation.
Let's get some terminology straight, generally when we say 'a model' we refer to a particular met | How to choose a predictive model after k-fold cross-validation?
I think that you are missing something still in your understanding of the purpose of cross-validation.
Let's get some terminology straight, generally when we say 'a model' we refer to a particular method for describing how some input data relates to what w... | How to choose a predictive model after k-fold cross-validation?
I think that you are missing something still in your understanding of the purpose of cross-validation.
Let's get some terminology straight, generally when we say 'a model' we refer to a particular met |
426 | How to choose a predictive model after k-fold cross-validation? | I found this excellent article How to Train a Final Machine Learning Model very helpful in clearing up all the confusions I have regarding the use of CV in machine learning.
Basically we use CV (e.g. 80/20 split, k-fold, etc) to estimate how well your whole procedure (including the data engineering, choice of model (... | How to choose a predictive model after k-fold cross-validation? | I found this excellent article How to Train a Final Machine Learning Model very helpful in clearing up all the confusions I have regarding the use of CV in machine learning.
Basically we use CV (e.g | How to choose a predictive model after k-fold cross-validation?
I found this excellent article How to Train a Final Machine Learning Model very helpful in clearing up all the confusions I have regarding the use of CV in machine learning.
Basically we use CV (e.g. 80/20 split, k-fold, etc) to estimate how well your wh... | How to choose a predictive model after k-fold cross-validation?
I found this excellent article How to Train a Final Machine Learning Model very helpful in clearing up all the confusions I have regarding the use of CV in machine learning.
Basically we use CV (e.g |
427 | How to choose a predictive model after k-fold cross-validation? | Let me throw in a few points in addition to Bogdanovist's answer
As you say, you train $k$ different models. They differ in that 1/(k-1)th of the training data is exchanged against other cases. These models are sometimes called surrogate models because the (average) performance measured for these models is taken as a s... | How to choose a predictive model after k-fold cross-validation? | Let me throw in a few points in addition to Bogdanovist's answer
As you say, you train $k$ different models. They differ in that 1/(k-1)th of the training data is exchanged against other cases. These | How to choose a predictive model after k-fold cross-validation?
Let me throw in a few points in addition to Bogdanovist's answer
As you say, you train $k$ different models. They differ in that 1/(k-1)th of the training data is exchanged against other cases. These models are sometimes called surrogate models because the... | How to choose a predictive model after k-fold cross-validation?
Let me throw in a few points in addition to Bogdanovist's answer
As you say, you train $k$ different models. They differ in that 1/(k-1)th of the training data is exchanged against other cases. These |
428 | How to choose a predictive model after k-fold cross-validation? | Why do we use k-fold cross validation?
Cross-validation is a method to estimate the skill of a method on unseen data. Like using a train-test split.
Cross-validation systematically creates and evaluates multiple models on multiple subsets of the dataset.
This, in turn, provides a population of performance measures.
We... | How to choose a predictive model after k-fold cross-validation? | Why do we use k-fold cross validation?
Cross-validation is a method to estimate the skill of a method on unseen data. Like using a train-test split.
Cross-validation systematically creates and evaluat | How to choose a predictive model after k-fold cross-validation?
Why do we use k-fold cross validation?
Cross-validation is a method to estimate the skill of a method on unseen data. Like using a train-test split.
Cross-validation systematically creates and evaluates multiple models on multiple subsets of the dataset.
T... | How to choose a predictive model after k-fold cross-validation?
Why do we use k-fold cross validation?
Cross-validation is a method to estimate the skill of a method on unseen data. Like using a train-test split.
Cross-validation systematically creates and evaluat |
429 | How to choose a predictive model after k-fold cross-validation? | It's a very interesting question. To make it clear, we should understand the difference of model and model evaluation. We use full training set to build a model, and we expect this model would be finally used.
K fold cross evaluation would build K models but all would be dropped. The K models are just used for evaluati... | How to choose a predictive model after k-fold cross-validation? | It's a very interesting question. To make it clear, we should understand the difference of model and model evaluation. We use full training set to build a model, and we expect this model would be fina | How to choose a predictive model after k-fold cross-validation?
It's a very interesting question. To make it clear, we should understand the difference of model and model evaluation. We use full training set to build a model, and we expect this model would be finally used.
K fold cross evaluation would build K models b... | How to choose a predictive model after k-fold cross-validation?
It's a very interesting question. To make it clear, we should understand the difference of model and model evaluation. We use full training set to build a model, and we expect this model would be fina |
430 | How to choose a predictive model after k-fold cross-validation? | I am not sure the discussion above is entirely correct. In cross-validation, we can split the data into Training and Testing for each run. Using the training data alone, one needs to fit the model and choose the tuning parameters in each class of models being considered. For example, in Neural Nets the tuning paramete... | How to choose a predictive model after k-fold cross-validation? | I am not sure the discussion above is entirely correct. In cross-validation, we can split the data into Training and Testing for each run. Using the training data alone, one needs to fit the model an | How to choose a predictive model after k-fold cross-validation?
I am not sure the discussion above is entirely correct. In cross-validation, we can split the data into Training and Testing for each run. Using the training data alone, one needs to fit the model and choose the tuning parameters in each class of models b... | How to choose a predictive model after k-fold cross-validation?
I am not sure the discussion above is entirely correct. In cross-validation, we can split the data into Training and Testing for each run. Using the training data alone, one needs to fit the model an |
431 | How to choose a predictive model after k-fold cross-validation? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
Even belatedly, let me throw in my 2 drachmas. I am of... | How to choose a predictive model after k-fold cross-validation? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| How to choose a predictive model after k-fold cross-validation?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | How to choose a predictive model after k-fold cross-validation?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
432 | How to choose a predictive model after k-fold cross-validation? | Consider two types of algorithms:
1. Algorithms come with hyperparameters, which do not change with different data subsets. Cross-validation might be used to evaluate
the performance of different algorithms
feature engineering
feature input
the selection of best hyperparameters.
However, with those algorithms (such a... | How to choose a predictive model after k-fold cross-validation? | Consider two types of algorithms:
1. Algorithms come with hyperparameters, which do not change with different data subsets. Cross-validation might be used to evaluate
the performance of different alg | How to choose a predictive model after k-fold cross-validation?
Consider two types of algorithms:
1. Algorithms come with hyperparameters, which do not change with different data subsets. Cross-validation might be used to evaluate
the performance of different algorithms
feature engineering
feature input
the selection ... | How to choose a predictive model after k-fold cross-validation?
Consider two types of algorithms:
1. Algorithms come with hyperparameters, which do not change with different data subsets. Cross-validation might be used to evaluate
the performance of different alg |
433 | Interpretation of R's lm() output | Five point summary
yes, the idea is to give a quick summary of the distribution. It should be roughly symmetrical about mean, the median should be close to 0, the 1Q and 3Q values should ideally be roughly similar values.
Coefficients and $\hat{\beta_i}s$
Each coefficient in the model is a Gaussian (Normal) random vari... | Interpretation of R's lm() output | Five point summary
yes, the idea is to give a quick summary of the distribution. It should be roughly symmetrical about mean, the median should be close to 0, the 1Q and 3Q values should ideally be ro | Interpretation of R's lm() output
Five point summary
yes, the idea is to give a quick summary of the distribution. It should be roughly symmetrical about mean, the median should be close to 0, the 1Q and 3Q values should ideally be roughly similar values.
Coefficients and $\hat{\beta_i}s$
Each coefficient in the model ... | Interpretation of R's lm() output
Five point summary
yes, the idea is to give a quick summary of the distribution. It should be roughly symmetrical about mean, the median should be close to 0, the 1Q and 3Q values should ideally be ro |
434 | Interpretation of R's lm() output | Ronen Israel and Adrienne Ross (AQR) wrote a very nice paper on this subject: Measuring Factor Exposures: Uses and Abuses.
To summarize (see: p. 8),
Generally, the higher the $R^2$ the better the model explains portfolio returns.
When the t-statistic is greater than two, we can say with 95% confidence (or a 5% chance ... | Interpretation of R's lm() output | Ronen Israel and Adrienne Ross (AQR) wrote a very nice paper on this subject: Measuring Factor Exposures: Uses and Abuses.
To summarize (see: p. 8),
Generally, the higher the $R^2$ the better the mod | Interpretation of R's lm() output
Ronen Israel and Adrienne Ross (AQR) wrote a very nice paper on this subject: Measuring Factor Exposures: Uses and Abuses.
To summarize (see: p. 8),
Generally, the higher the $R^2$ the better the model explains portfolio returns.
When the t-statistic is greater than two, we can say wi... | Interpretation of R's lm() output
Ronen Israel and Adrienne Ross (AQR) wrote a very nice paper on this subject: Measuring Factor Exposures: Uses and Abuses.
To summarize (see: p. 8),
Generally, the higher the $R^2$ the better the mod |
435 | How would you explain covariance to someone who understands only the mean? | Sometimes we can "augment knowledge" with an unusual or different approach. I would like this reply to be accessible to kindergartners and also have some fun, so everybody get out your crayons!
Given paired $(x,y)$ data, draw their scatterplot. (The younger students may need a teacher to produce this for them. :-) E... | How would you explain covariance to someone who understands only the mean? | Sometimes we can "augment knowledge" with an unusual or different approach. I would like this reply to be accessible to kindergartners and also have some fun, so everybody get out your crayons!
Given | How would you explain covariance to someone who understands only the mean?
Sometimes we can "augment knowledge" with an unusual or different approach. I would like this reply to be accessible to kindergartners and also have some fun, so everybody get out your crayons!
Given paired $(x,y)$ data, draw their scatterplot.... | How would you explain covariance to someone who understands only the mean?
Sometimes we can "augment knowledge" with an unusual or different approach. I would like this reply to be accessible to kindergartners and also have some fun, so everybody get out your crayons!
Given |
436 | How would you explain covariance to someone who understands only the mean? | To elaborate on my comment, I used to teach the covariance as a measure of the (average) co-variation between two variables, say $x$ and $y$.
It is useful to recall the basic formula (simple to explain, no need to talk about mathematical expectancies for an introductory course):
$$
\text{cov}(x,y)=\frac{1}{n}\sum_{i=1... | How would you explain covariance to someone who understands only the mean? | To elaborate on my comment, I used to teach the covariance as a measure of the (average) co-variation between two variables, say $x$ and $y$.
It is useful to recall the basic formula (simple to expla | How would you explain covariance to someone who understands only the mean?
To elaborate on my comment, I used to teach the covariance as a measure of the (average) co-variation between two variables, say $x$ and $y$.
It is useful to recall the basic formula (simple to explain, no need to talk about mathematical expect... | How would you explain covariance to someone who understands only the mean?
To elaborate on my comment, I used to teach the covariance as a measure of the (average) co-variation between two variables, say $x$ and $y$.
It is useful to recall the basic formula (simple to expla |
437 | How would you explain covariance to someone who understands only the mean? | I loved @whuber 's answer - before I only had a vague idea in my mind of how covariance could be visualised, but those rectangle plots are genius.
However since the formula for covariance involves the mean, and the OP's original question did state that the 'receiver' does understand the concept of the mean, I thought ... | How would you explain covariance to someone who understands only the mean? | I loved @whuber 's answer - before I only had a vague idea in my mind of how covariance could be visualised, but those rectangle plots are genius.
However since the formula for covariance involves th | How would you explain covariance to someone who understands only the mean?
I loved @whuber 's answer - before I only had a vague idea in my mind of how covariance could be visualised, but those rectangle plots are genius.
However since the formula for covariance involves the mean, and the OP's original question did st... | How would you explain covariance to someone who understands only the mean?
I loved @whuber 's answer - before I only had a vague idea in my mind of how covariance could be visualised, but those rectangle plots are genius.
However since the formula for covariance involves th |
438 | How would you explain covariance to someone who understands only the mean? | Covariance is a measure of how much one variable goes up when the other goes up. | How would you explain covariance to someone who understands only the mean? | Covariance is a measure of how much one variable goes up when the other goes up. | How would you explain covariance to someone who understands only the mean?
Covariance is a measure of how much one variable goes up when the other goes up. | How would you explain covariance to someone who understands only the mean?
Covariance is a measure of how much one variable goes up when the other goes up. |
439 | How would you explain covariance to someone who understands only the mean? | I am answering my own question, but I thought It'd be great for the people coming across this post to check out some of the explanations on this page.
I'm paraphrasing one of the very well articulated answers (by a user'Zhop'). I'm doing so in case if that site shuts down or the page gets taken down when someone eons f... | How would you explain covariance to someone who understands only the mean? | I am answering my own question, but I thought It'd be great for the people coming across this post to check out some of the explanations on this page.
I'm paraphrasing one of the very well articulated | How would you explain covariance to someone who understands only the mean?
I am answering my own question, but I thought It'd be great for the people coming across this post to check out some of the explanations on this page.
I'm paraphrasing one of the very well articulated answers (by a user'Zhop'). I'm doing so in c... | How would you explain covariance to someone who understands only the mean?
I am answering my own question, but I thought It'd be great for the people coming across this post to check out some of the explanations on this page.
I'm paraphrasing one of the very well articulated |
440 | How would you explain covariance to someone who understands only the mean? | I really like Whuber's answer, so I gathered some more resources. Covariance describes both how far the variables are spread out, and the nature of their relationship.
Covariance uses rectangles to describe how far away an observation is from the mean on a scatter graph:
If a rectangle has long sides and a high width ... | How would you explain covariance to someone who understands only the mean? | I really like Whuber's answer, so I gathered some more resources. Covariance describes both how far the variables are spread out, and the nature of their relationship.
Covariance uses rectangles to de | How would you explain covariance to someone who understands only the mean?
I really like Whuber's answer, so I gathered some more resources. Covariance describes both how far the variables are spread out, and the nature of their relationship.
Covariance uses rectangles to describe how far away an observation is from th... | How would you explain covariance to someone who understands only the mean?
I really like Whuber's answer, so I gathered some more resources. Covariance describes both how far the variables are spread out, and the nature of their relationship.
Covariance uses rectangles to de |
441 | How would you explain covariance to someone who understands only the mean? | Here's another attempt to explain covariance with a picture. Every panel in the picture below contains 50 points simulated from a bivariate distribution with correlation between x & y of 0.8 and variances as shown in the row and column labels. The covariance is shown in the lower-right corner of each panel.
Anyone i... | How would you explain covariance to someone who understands only the mean? | Here's another attempt to explain covariance with a picture. Every panel in the picture below contains 50 points simulated from a bivariate distribution with correlation between x & y of 0.8 and vari | How would you explain covariance to someone who understands only the mean?
Here's another attempt to explain covariance with a picture. Every panel in the picture below contains 50 points simulated from a bivariate distribution with correlation between x & y of 0.8 and variances as shown in the row and column labels. ... | How would you explain covariance to someone who understands only the mean?
Here's another attempt to explain covariance with a picture. Every panel in the picture below contains 50 points simulated from a bivariate distribution with correlation between x & y of 0.8 and vari |
442 | How would you explain covariance to someone who understands only the mean? | I would simply explain correlation which is pretty intuitive. I would say "Correlation measures the strength of relationship between two variables X and Y. Correlation is between -1 and 1 and will be close to 1 in absolute value when the relationship is strong. Covariance is just the correlation multiplied by the st... | How would you explain covariance to someone who understands only the mean? | I would simply explain correlation which is pretty intuitive. I would say "Correlation measures the strength of relationship between two variables X and Y. Correlation is between -1 and 1 and will b | How would you explain covariance to someone who understands only the mean?
I would simply explain correlation which is pretty intuitive. I would say "Correlation measures the strength of relationship between two variables X and Y. Correlation is between -1 and 1 and will be close to 1 in absolute value when the relat... | How would you explain covariance to someone who understands only the mean?
I would simply explain correlation which is pretty intuitive. I would say "Correlation measures the strength of relationship between two variables X and Y. Correlation is between -1 and 1 and will b |
443 | How would you explain covariance to someone who understands only the mean? | Variance is the degree by which a random vairable changes with respect to its expected value Owing to the stochastic nature of be underlying process the random variable represents.
Covariance is the degree by which two different random variables change with respect to each other. This could happen when random variables... | How would you explain covariance to someone who understands only the mean? | Variance is the degree by which a random vairable changes with respect to its expected value Owing to the stochastic nature of be underlying process the random variable represents.
Covariance is the d | How would you explain covariance to someone who understands only the mean?
Variance is the degree by which a random vairable changes with respect to its expected value Owing to the stochastic nature of be underlying process the random variable represents.
Covariance is the degree by which two different random variables... | How would you explain covariance to someone who understands only the mean?
Variance is the degree by which a random vairable changes with respect to its expected value Owing to the stochastic nature of be underlying process the random variable represents.
Covariance is the d |
444 | How would you explain covariance to someone who understands only the mean? | Two variables that would have a high positive covariance (correlation) would be the number of people in a room, and the number of fingers that are in the room. (As the number of people increases, we expect the number of fingers to increase as well.)
Something that might have a negative covariance (correlation) would... | How would you explain covariance to someone who understands only the mean? | Two variables that would have a high positive covariance (correlation) would be the number of people in a room, and the number of fingers that are in the room. (As the number of people increases, we | How would you explain covariance to someone who understands only the mean?
Two variables that would have a high positive covariance (correlation) would be the number of people in a room, and the number of fingers that are in the room. (As the number of people increases, we expect the number of fingers to increase as w... | How would you explain covariance to someone who understands only the mean?
Two variables that would have a high positive covariance (correlation) would be the number of people in a room, and the number of fingers that are in the room. (As the number of people increases, we |
445 | How would you explain covariance to someone who understands only the mean? | Covariance is a statistical measure that describes the relationship between two variables. If two variables have a positive covariance, it means that they tend to increase or decrease together. If they have a negative covariance, it means that they tend to move in opposite directions. If they have a covariance of zero,... | How would you explain covariance to someone who understands only the mean? | Covariance is a statistical measure that describes the relationship between two variables. If two variables have a positive covariance, it means that they tend to increase or decrease together. If the | How would you explain covariance to someone who understands only the mean?
Covariance is a statistical measure that describes the relationship between two variables. If two variables have a positive covariance, it means that they tend to increase or decrease together. If they have a negative covariance, it means that t... | How would you explain covariance to someone who understands only the mean?
Covariance is a statistical measure that describes the relationship between two variables. If two variables have a positive covariance, it means that they tend to increase or decrease together. If the |
446 | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | First, we need to understand what is a Markov chain. Consider the following weather example from Wikipedia. Suppose that weather on any given day can be classified into two states only: sunny and rainy. Based on past experience, we know the following:
$P(\text{Next day is Sunny}\,\vert \,\text{Given today is Rainy)}=0.... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | First, we need to understand what is a Markov chain. Consider the following weather example from Wikipedia. Suppose that weather on any given day can be classified into two states only: sunny and rain | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
First, we need to understand what is a Markov chain. Consider the following weather example from Wikipedia. Suppose that weather on any given day can be classified into two states only: sunny and rainy. Based on past experience, we know the following... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
First, we need to understand what is a Markov chain. Consider the following weather example from Wikipedia. Suppose that weather on any given day can be classified into two states only: sunny and rain |
447 | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | I think there's a nice and simple intuition to be gained from the (independence-chain) Metropolis-Hastings algorithm.
First, what's the goal? The goal of MCMC is to draw samples from some probability distribution without having to know its exact height at any point. The way MCMC achieves this is to "wander around" on... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | I think there's a nice and simple intuition to be gained from the (independence-chain) Metropolis-Hastings algorithm.
First, what's the goal? The goal of MCMC is to draw samples from some probability | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
I think there's a nice and simple intuition to be gained from the (independence-chain) Metropolis-Hastings algorithm.
First, what's the goal? The goal of MCMC is to draw samples from some probability distribution without having to know its exact hei... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
I think there's a nice and simple intuition to be gained from the (independence-chain) Metropolis-Hastings algorithm.
First, what's the goal? The goal of MCMC is to draw samples from some probability |
448 | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | I'd probably say something like this:
"Anytime we want to talk about probabilities, we're really integrating a density. In Bayesian analysis, a lot of the densities we come up with aren't analytically tractable: you can only integrate them -- if you can integrate them at all -- with a great deal of suffering. So what... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | I'd probably say something like this:
"Anytime we want to talk about probabilities, we're really integrating a density. In Bayesian analysis, a lot of the densities we come up with aren't analyticall | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
I'd probably say something like this:
"Anytime we want to talk about probabilities, we're really integrating a density. In Bayesian analysis, a lot of the densities we come up with aren't analytically tractable: you can only integrate them -- if you... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
I'd probably say something like this:
"Anytime we want to talk about probabilities, we're really integrating a density. In Bayesian analysis, a lot of the densities we come up with aren't analyticall |
449 | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | Imagine you want to find a better strategy to beat your friends at the board game Monopoly. Simplify the stuff that matters in the game to the question: which properties do people land on most? The answer depends on the structure of the board, the rules of the game and the throws of two dice.
One way to answer the que... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | Imagine you want to find a better strategy to beat your friends at the board game Monopoly. Simplify the stuff that matters in the game to the question: which properties do people land on most? The an | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
Imagine you want to find a better strategy to beat your friends at the board game Monopoly. Simplify the stuff that matters in the game to the question: which properties do people land on most? The answer depends on the structure of the board, the ru... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
Imagine you want to find a better strategy to beat your friends at the board game Monopoly. Simplify the stuff that matters in the game to the question: which properties do people land on most? The an |
450 | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | OK here's my best attempt at an informal and crude explanation.
A Markov Chain is a random process that has the property that the future depends only on the current state of the process and not the past i.e. it is memoryless. An example of a random process could be the stock exchange. An example of a Markov Chain would... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | OK here's my best attempt at an informal and crude explanation.
A Markov Chain is a random process that has the property that the future depends only on the current state of the process and not the pa | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
OK here's my best attempt at an informal and crude explanation.
A Markov Chain is a random process that has the property that the future depends only on the current state of the process and not the past i.e. it is memoryless. An example of a random p... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
OK here's my best attempt at an informal and crude explanation.
A Markov Chain is a random process that has the property that the future depends only on the current state of the process and not the pa |
451 | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | Excerpt from Bayesian Methods for Hackers
The Bayesian landscape
When we setup a Bayesian inference problem with $N$ unknowns, we are implicitly creating a $N$ dimensional space for the prior distributions to exist in. Associated with the space is an additional dimension, which we can describe as the surface, or curve,... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | Excerpt from Bayesian Methods for Hackers
The Bayesian landscape
When we setup a Bayesian inference problem with $N$ unknowns, we are implicitly creating a $N$ dimensional space for the prior distribu | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
Excerpt from Bayesian Methods for Hackers
The Bayesian landscape
When we setup a Bayesian inference problem with $N$ unknowns, we are implicitly creating a $N$ dimensional space for the prior distributions to exist in. Associated with the space is an... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
Excerpt from Bayesian Methods for Hackers
The Bayesian landscape
When we setup a Bayesian inference problem with $N$ unknowns, we are implicitly creating a $N$ dimensional space for the prior distribu |
452 | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | So there are plenty of answers here paraphrased from statistics/probability textbooks, Wikipedia, etc. I believe we have "laypersons" where I work; I think they are in the marketing department. If I ever have to explain anything technical to them, I apply the rule "show don't tell." With that rule in mind, I would prob... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | So there are plenty of answers here paraphrased from statistics/probability textbooks, Wikipedia, etc. I believe we have "laypersons" where I work; I think they are in the marketing department. If I e | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
So there are plenty of answers here paraphrased from statistics/probability textbooks, Wikipedia, etc. I believe we have "laypersons" where I work; I think they are in the marketing department. If I ever have to explain anything technical to them, I ... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
So there are plenty of answers here paraphrased from statistics/probability textbooks, Wikipedia, etc. I believe we have "laypersons" where I work; I think they are in the marketing department. If I e |
453 | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | MCMC is typically used as an alternative to crude Monte Carlo simulation techniques. Both MCMC and other Monte Carlo techniques are used to evaluate difficult integrals but MCMC can be used more generally.
For example, a common problem in statistics is to calculate the mean outcome relating to some probabilistic/stocha... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | MCMC is typically used as an alternative to crude Monte Carlo simulation techniques. Both MCMC and other Monte Carlo techniques are used to evaluate difficult integrals but MCMC can be used more gener | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
MCMC is typically used as an alternative to crude Monte Carlo simulation techniques. Both MCMC and other Monte Carlo techniques are used to evaluate difficult integrals but MCMC can be used more generally.
For example, a common problem in statistics ... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
MCMC is typically used as an alternative to crude Monte Carlo simulation techniques. Both MCMC and other Monte Carlo techniques are used to evaluate difficult integrals but MCMC can be used more gener |
454 | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | I'm a DNA analyst that uses fully continuous probabilistic genotyping software to interpret DNA evidence and I have to explain how this works to a jury. Admittedly, we over simplify and I realize some of this over simplification sacrifices accuracy of specific details in the name of improving overall understanding. But... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | I'm a DNA analyst that uses fully continuous probabilistic genotyping software to interpret DNA evidence and I have to explain how this works to a jury. Admittedly, we over simplify and I realize some | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
I'm a DNA analyst that uses fully continuous probabilistic genotyping software to interpret DNA evidence and I have to explain how this works to a jury. Admittedly, we over simplify and I realize some of this over simplification sacrifices accuracy o... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
I'm a DNA analyst that uses fully continuous probabilistic genotyping software to interpret DNA evidence and I have to explain how this works to a jury. Admittedly, we over simplify and I realize some |
455 | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | This question is broad yet the answers are often quite casual. Alternatively, you can see this paper which gives a concise mathematical description of a broad class of MCMC algorithms including Metropolis-Hastings algorithms, Gibbs sampling, Metropolis-within-Gibbs and auxiliary variables methods, slice sampling, recur... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | This question is broad yet the answers are often quite casual. Alternatively, you can see this paper which gives a concise mathematical description of a broad class of MCMC algorithms including Metrop | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
This question is broad yet the answers are often quite casual. Alternatively, you can see this paper which gives a concise mathematical description of a broad class of MCMC algorithms including Metropolis-Hastings algorithms, Gibbs sampling, Metropol... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
This question is broad yet the answers are often quite casual. Alternatively, you can see this paper which gives a concise mathematical description of a broad class of MCMC algorithms including Metrop |
456 | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | First, we should explain Monte-Carlo sampling to the layperson. Imagine when you don't have the exact form of a function (for example, $f(x,y)=z=x^2+2*x*y$) but there is a machine in Europe (and Los Alamos) that replicates this function (numerically). We can put as many $(x,y)$ pairs into it and it will give us the val... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | First, we should explain Monte-Carlo sampling to the layperson. Imagine when you don't have the exact form of a function (for example, $f(x,y)=z=x^2+2*x*y$) but there is a machine in Europe (and Los A | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
First, we should explain Monte-Carlo sampling to the layperson. Imagine when you don't have the exact form of a function (for example, $f(x,y)=z=x^2+2*x*y$) but there is a machine in Europe (and Los Alamos) that replicates this function (numerically)... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
First, we should explain Monte-Carlo sampling to the layperson. Imagine when you don't have the exact form of a function (for example, $f(x,y)=z=x^2+2*x*y$) but there is a machine in Europe (and Los A |
457 | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | We want to know the posterior distribution $P(\theta)$ and where modes are, this is the goal.
But we cannot calculate $P(\theta)$ analytically, this is the problem.
However, we can build a Markov Chain.
Sampling from the Markov Chain builds the histogram, and
The histogram approximates $P(\theta)$, this is the solutio... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson? | We want to know the posterior distribution $P(\theta)$ and where modes are, this is the goal.
But we cannot calculate $P(\theta)$ analytically, this is the problem.
However, we can build a Markov Cha | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
We want to know the posterior distribution $P(\theta)$ and where modes are, this is the goal.
But we cannot calculate $P(\theta)$ analytically, this is the problem.
However, we can build a Markov Chain.
Sampling from the Markov Chain builds the hist... | How would you explain Markov Chain Monte Carlo (MCMC) to a layperson?
We want to know the posterior distribution $P(\theta)$ and where modes are, this is the goal.
But we cannot calculate $P(\theta)$ analytically, this is the problem.
However, we can build a Markov Cha |
458 | How to know that your machine learning problem is hopeless? | Forecastability
You are right that this is a question of forecastability. There have been a few articles on forecastability in the IIF's practitioner-oriented journal Foresight. (Full disclosure: I'm an Associate Editor.)
The problem is that forecastability is already hard to assess in "simple" cases.
A few examples
Su... | How to know that your machine learning problem is hopeless? | Forecastability
You are right that this is a question of forecastability. There have been a few articles on forecastability in the IIF's practitioner-oriented journal Foresight. (Full disclosure: I'm | How to know that your machine learning problem is hopeless?
Forecastability
You are right that this is a question of forecastability. There have been a few articles on forecastability in the IIF's practitioner-oriented journal Foresight. (Full disclosure: I'm an Associate Editor.)
The problem is that forecastability is... | How to know that your machine learning problem is hopeless?
Forecastability
You are right that this is a question of forecastability. There have been a few articles on forecastability in the IIF's practitioner-oriented journal Foresight. (Full disclosure: I'm |
459 | How to know that your machine learning problem is hopeless? | The answer from Stephan Kolassa is excellent, but I would like to add that there is also often an economic stop condition:
When you are doing ML for a customer and not for fun, you should take a look at the amount of money the customer is willing to spend. If he pays your firm 5000€ and you spent a month on finding a ... | How to know that your machine learning problem is hopeless? | The answer from Stephan Kolassa is excellent, but I would like to add that there is also often an economic stop condition:
When you are doing ML for a customer and not for fun, you should take a look | How to know that your machine learning problem is hopeless?
The answer from Stephan Kolassa is excellent, but I would like to add that there is also often an economic stop condition:
When you are doing ML for a customer and not for fun, you should take a look at the amount of money the customer is willing to spend. If... | How to know that your machine learning problem is hopeless?
The answer from Stephan Kolassa is excellent, but I would like to add that there is also often an economic stop condition:
When you are doing ML for a customer and not for fun, you should take a look |
460 | How to know that your machine learning problem is hopeless? | There is another way. Ask yourself -
Who or what makes the best possible forecasts of this particular variable?"
Does my machine learning algorithm produce better or worse results than the best forecasts?
So, for example, if you had a large number of variables associated with different soccer teams and you were ... | How to know that your machine learning problem is hopeless? | There is another way. Ask yourself -
Who or what makes the best possible forecasts of this particular variable?"
Does my machine learning algorithm produce better or worse results than the best fo | How to know that your machine learning problem is hopeless?
There is another way. Ask yourself -
Who or what makes the best possible forecasts of this particular variable?"
Does my machine learning algorithm produce better or worse results than the best forecasts?
So, for example, if you had a large number of va... | How to know that your machine learning problem is hopeless?
There is another way. Ask yourself -
Who or what makes the best possible forecasts of this particular variable?"
Does my machine learning algorithm produce better or worse results than the best fo |
461 | What are the differences between Factor Analysis and Principal Component Analysis? | Principal component analysis involves extracting linear composites of observed variables.
Factor analysis is based on a formal model predicting observed variables from theoretical latent factors.
In psychology these two techniques are often applied in the construction of multi-scale tests
to determine which items load... | What are the differences between Factor Analysis and Principal Component Analysis? | Principal component analysis involves extracting linear composites of observed variables.
Factor analysis is based on a formal model predicting observed variables from theoretical latent factors.
In p | What are the differences between Factor Analysis and Principal Component Analysis?
Principal component analysis involves extracting linear composites of observed variables.
Factor analysis is based on a formal model predicting observed variables from theoretical latent factors.
In psychology these two techniques are of... | What are the differences between Factor Analysis and Principal Component Analysis?
Principal component analysis involves extracting linear composites of observed variables.
Factor analysis is based on a formal model predicting observed variables from theoretical latent factors.
In p |
462 | What are the differences between Factor Analysis and Principal Component Analysis? | From my response here:
Is PCA followed by a rotation (such as varimax) still PCA?
Principal Component Analysis (PCA) and Common Factor Analysis (CFA) are distinct methods. Often, they produce similar results and PCA is used as the default extraction method in the SPSS Factor Analysis routines. This undoubtedly results ... | What are the differences between Factor Analysis and Principal Component Analysis? | From my response here:
Is PCA followed by a rotation (such as varimax) still PCA?
Principal Component Analysis (PCA) and Common Factor Analysis (CFA) are distinct methods. Often, they produce similar | What are the differences between Factor Analysis and Principal Component Analysis?
From my response here:
Is PCA followed by a rotation (such as varimax) still PCA?
Principal Component Analysis (PCA) and Common Factor Analysis (CFA) are distinct methods. Often, they produce similar results and PCA is used as the defaul... | What are the differences between Factor Analysis and Principal Component Analysis?
From my response here:
Is PCA followed by a rotation (such as varimax) still PCA?
Principal Component Analysis (PCA) and Common Factor Analysis (CFA) are distinct methods. Often, they produce similar |
463 | What are the differences between Factor Analysis and Principal Component Analysis? | A basic, yet a kind of painstaking, explanation of PCA vs Factor analysis with the help of scatterplots, in logical steps. (I thank @amoeba who, in his comment to the question, has encouraged me to post an answer in place of making links to elsewhere. So here is a leisure, late response.)
PCA as variable summarization ... | What are the differences between Factor Analysis and Principal Component Analysis? | A basic, yet a kind of painstaking, explanation of PCA vs Factor analysis with the help of scatterplots, in logical steps. (I thank @amoeba who, in his comment to the question, has encouraged me to po | What are the differences between Factor Analysis and Principal Component Analysis?
A basic, yet a kind of painstaking, explanation of PCA vs Factor analysis with the help of scatterplots, in logical steps. (I thank @amoeba who, in his comment to the question, has encouraged me to post an answer in place of making links... | What are the differences between Factor Analysis and Principal Component Analysis?
A basic, yet a kind of painstaking, explanation of PCA vs Factor analysis with the help of scatterplots, in logical steps. (I thank @amoeba who, in his comment to the question, has encouraged me to po |
464 | What are the differences between Factor Analysis and Principal Component Analysis? | The top answer in this thread suggests that PCA is more of a dimensionality reduction technique, whereas FA is more of a latent variable technique. This is sensu stricto correct. But many answers here and many treatments elsewhere present PCA and FA as two completely different methods, with dissimilar if not opposite g... | What are the differences between Factor Analysis and Principal Component Analysis? | The top answer in this thread suggests that PCA is more of a dimensionality reduction technique, whereas FA is more of a latent variable technique. This is sensu stricto correct. But many answers here | What are the differences between Factor Analysis and Principal Component Analysis?
The top answer in this thread suggests that PCA is more of a dimensionality reduction technique, whereas FA is more of a latent variable technique. This is sensu stricto correct. But many answers here and many treatments elsewhere presen... | What are the differences between Factor Analysis and Principal Component Analysis?
The top answer in this thread suggests that PCA is more of a dimensionality reduction technique, whereas FA is more of a latent variable technique. This is sensu stricto correct. But many answers here |
465 | What are the differences between Factor Analysis and Principal Component Analysis? | There are numerous suggested definitions on the web. Here is one from a on-line glossary on statistical learning:
Principal Component Analysis
Constructing new features which are
the principal components of a data
set. The principal components are
random variables of maximal variance
constructed from linear co... | What are the differences between Factor Analysis and Principal Component Analysis? | There are numerous suggested definitions on the web. Here is one from a on-line glossary on statistical learning:
Principal Component Analysis
Constructing new features which are
the principal comp | What are the differences between Factor Analysis and Principal Component Analysis?
There are numerous suggested definitions on the web. Here is one from a on-line glossary on statistical learning:
Principal Component Analysis
Constructing new features which are
the principal components of a data
set. The principal... | What are the differences between Factor Analysis and Principal Component Analysis?
There are numerous suggested definitions on the web. Here is one from a on-line glossary on statistical learning:
Principal Component Analysis
Constructing new features which are
the principal comp |
466 | What are the differences between Factor Analysis and Principal Component Analysis? | You are right about your first point, although in FA you generally work with both (uniqueness and communality).
The choice between PCA and FA is a long-standing debate among psychometricians. I don't quite follow your points, though. Rotation of principal axes can be applied whatever the method is used to construct lat... | What are the differences between Factor Analysis and Principal Component Analysis? | You are right about your first point, although in FA you generally work with both (uniqueness and communality).
The choice between PCA and FA is a long-standing debate among psychometricians. I don't | What are the differences between Factor Analysis and Principal Component Analysis?
You are right about your first point, although in FA you generally work with both (uniqueness and communality).
The choice between PCA and FA is a long-standing debate among psychometricians. I don't quite follow your points, though. Rot... | What are the differences between Factor Analysis and Principal Component Analysis?
You are right about your first point, although in FA you generally work with both (uniqueness and communality).
The choice between PCA and FA is a long-standing debate among psychometricians. I don't |
467 | What are the differences between Factor Analysis and Principal Component Analysis? | Differences between factor analysis and principal component analysis are:
• In factor analysis there is a structured model and some assumptions. In this respect it is a statistical technique which does not apply to principal component analysis which is a purely mathematical transformation.
• The aim of principal compon... | What are the differences between Factor Analysis and Principal Component Analysis? | Differences between factor analysis and principal component analysis are:
• In factor analysis there is a structured model and some assumptions. In this respect it is a statistical technique which doe | What are the differences between Factor Analysis and Principal Component Analysis?
Differences between factor analysis and principal component analysis are:
• In factor analysis there is a structured model and some assumptions. In this respect it is a statistical technique which does not apply to principal component an... | What are the differences between Factor Analysis and Principal Component Analysis?
Differences between factor analysis and principal component analysis are:
• In factor analysis there is a structured model and some assumptions. In this respect it is a statistical technique which doe |
468 | What are the differences between Factor Analysis and Principal Component Analysis? | For me (and I hope this is useful) factor analysis is much more useful than PCA.
Recently, I had the pleasure of analysing a scale through factor analysis. This scale (although it's widely used in industry) was developed by using PCA, and to my knowledge had never been factor analysed.
When I performed the factor ana... | What are the differences between Factor Analysis and Principal Component Analysis? | For me (and I hope this is useful) factor analysis is much more useful than PCA.
Recently, I had the pleasure of analysing a scale through factor analysis. This scale (although it's widely used in in | What are the differences between Factor Analysis and Principal Component Analysis?
For me (and I hope this is useful) factor analysis is much more useful than PCA.
Recently, I had the pleasure of analysing a scale through factor analysis. This scale (although it's widely used in industry) was developed by using PCA, a... | What are the differences between Factor Analysis and Principal Component Analysis?
For me (and I hope this is useful) factor analysis is much more useful than PCA.
Recently, I had the pleasure of analysing a scale through factor analysis. This scale (although it's widely used in in |
469 | What are the differences between Factor Analysis and Principal Component Analysis? | A quote from a really nice textbook (Brown, 2006, pp. 22, emphasis added).
PCA = principal components analysis
EFA = exploratory factor analysis
CFA = confirmatory factor analysis
Although related to EFA, principal components analysis (PCA) is frequently miscategorized as an estimation method of common factor analysis... | What are the differences between Factor Analysis and Principal Component Analysis? | A quote from a really nice textbook (Brown, 2006, pp. 22, emphasis added).
PCA = principal components analysis
EFA = exploratory factor analysis
CFA = confirmatory factor analysis
Although related to | What are the differences between Factor Analysis and Principal Component Analysis?
A quote from a really nice textbook (Brown, 2006, pp. 22, emphasis added).
PCA = principal components analysis
EFA = exploratory factor analysis
CFA = confirmatory factor analysis
Although related to EFA, principal components analysis (... | What are the differences between Factor Analysis and Principal Component Analysis?
A quote from a really nice textbook (Brown, 2006, pp. 22, emphasis added).
PCA = principal components analysis
EFA = exploratory factor analysis
CFA = confirmatory factor analysis
Although related to |
470 | What are the differences between Factor Analysis and Principal Component Analysis? | Expanding on @StatisticsDocConsulting's answer: the difference in loadings between EFA and PCA is non-trivial with a small number of variables. Here's a simulation function to demonstrate this in R:
simtestit=function(Sample.Size=1000,n.Variables=3,n.Factors=1,Iterations=100)
{require(psych);X=list();x=matrix(NA,nrow=S... | What are the differences between Factor Analysis and Principal Component Analysis? | Expanding on @StatisticsDocConsulting's answer: the difference in loadings between EFA and PCA is non-trivial with a small number of variables. Here's a simulation function to demonstrate this in R:
s | What are the differences between Factor Analysis and Principal Component Analysis?
Expanding on @StatisticsDocConsulting's answer: the difference in loadings between EFA and PCA is non-trivial with a small number of variables. Here's a simulation function to demonstrate this in R:
simtestit=function(Sample.Size=1000,n.... | What are the differences between Factor Analysis and Principal Component Analysis?
Expanding on @StatisticsDocConsulting's answer: the difference in loadings between EFA and PCA is non-trivial with a small number of variables. Here's a simulation function to demonstrate this in R:
s |
471 | What are the differences between Factor Analysis and Principal Component Analysis? | One can think of a PCA as being like a FA in which the communalities are assumed to equal 1 for all variables. In practice, this means that items that would have relatively low factor loadings in FA due to low communality will have higher loadings in PCA. This is not a desirable feature if the primary purpose of the ... | What are the differences between Factor Analysis and Principal Component Analysis? | One can think of a PCA as being like a FA in which the communalities are assumed to equal 1 for all variables. In practice, this means that items that would have relatively low factor loadings in FA | What are the differences between Factor Analysis and Principal Component Analysis?
One can think of a PCA as being like a FA in which the communalities are assumed to equal 1 for all variables. In practice, this means that items that would have relatively low factor loadings in FA due to low communality will have high... | What are the differences between Factor Analysis and Principal Component Analysis?
One can think of a PCA as being like a FA in which the communalities are assumed to equal 1 for all variables. In practice, this means that items that would have relatively low factor loadings in FA |
472 | What are the differences between Factor Analysis and Principal Component Analysis? | In a paper by Tipping and Bischop the tight relationship between Probabalistic PCA (PPCA) and Factor analysis is discussed. PPCA is closer to FA than the classic PCA is. The common model is
$$\mathbf{y} = \mu + \mathbf{Wx} + \epsilon$$
where $\mathbf{W} \in \mathbb{R}^{p,d}$, $\mathbf{x} \sim \mathcal{N}(\mathbf{0},\ma... | What are the differences between Factor Analysis and Principal Component Analysis? | In a paper by Tipping and Bischop the tight relationship between Probabalistic PCA (PPCA) and Factor analysis is discussed. PPCA is closer to FA than the classic PCA is. The common model is
$$\mathbf{ | What are the differences between Factor Analysis and Principal Component Analysis?
In a paper by Tipping and Bischop the tight relationship between Probabalistic PCA (PPCA) and Factor analysis is discussed. PPCA is closer to FA than the classic PCA is. The common model is
$$\mathbf{y} = \mu + \mathbf{Wx} + \epsilon$$
w... | What are the differences between Factor Analysis and Principal Component Analysis?
In a paper by Tipping and Bischop the tight relationship between Probabalistic PCA (PPCA) and Factor analysis is discussed. PPCA is closer to FA than the classic PCA is. The common model is
$$\mathbf{ |
473 | What are the differences between Factor Analysis and Principal Component Analysis? | None of these response is perfect. Either FA or PCA has some variants. We must clearly point out which variants are compared.
I would compare the maximum likelihood factor analysis and the Hotelling's PCA.
The former assume the latent variable follow a normal distribution but PCA has no such an assumption. This has le... | What are the differences between Factor Analysis and Principal Component Analysis? | None of these response is perfect. Either FA or PCA has some variants. We must clearly point out which variants are compared.
I would compare the maximum likelihood factor analysis and the Hotelling' | What are the differences between Factor Analysis and Principal Component Analysis?
None of these response is perfect. Either FA or PCA has some variants. We must clearly point out which variants are compared.
I would compare the maximum likelihood factor analysis and the Hotelling's PCA.
The former assume the latent v... | What are the differences between Factor Analysis and Principal Component Analysis?
None of these response is perfect. Either FA or PCA has some variants. We must clearly point out which variants are compared.
I would compare the maximum likelihood factor analysis and the Hotelling' |
474 | What are the differences between Factor Analysis and Principal Component Analysis? | There many great answers for this post but recently, I came across another difference.
Clustering is one application where PCA and FA yield different results. When there are many features in the data, one may be attempted to find the top PC directions and project the data on these PCs, then proceed with clustering. Of... | What are the differences between Factor Analysis and Principal Component Analysis? | There many great answers for this post but recently, I came across another difference.
Clustering is one application where PCA and FA yield different results. When there are many features in the data | What are the differences between Factor Analysis and Principal Component Analysis?
There many great answers for this post but recently, I came across another difference.
Clustering is one application where PCA and FA yield different results. When there are many features in the data, one may be attempted to find the to... | What are the differences between Factor Analysis and Principal Component Analysis?
There many great answers for this post but recently, I came across another difference.
Clustering is one application where PCA and FA yield different results. When there are many features in the data |
475 | What are the differences between Factor Analysis and Principal Component Analysis? | From Factor Analysis Vs. PCA (Principal Component Analysis)
Principal Component Analysis
Factor Analysis
Meaning
A component is a derived new dimension (or variable) so that the derived variables are linearly independent of each other.
A factor (or latent) is a common or underlying element with which several o... | What are the differences between Factor Analysis and Principal Component Analysis? | From Factor Analysis Vs. PCA (Principal Component Analysis)
Principal Component Analysis
Factor Analysis
Meaning
A component is a derived new dimension (or variable) so that the derived varia | What are the differences between Factor Analysis and Principal Component Analysis?
From Factor Analysis Vs. PCA (Principal Component Analysis)
Principal Component Analysis
Factor Analysis
Meaning
A component is a derived new dimension (or variable) so that the derived variables are linearly independent of each... | What are the differences between Factor Analysis and Principal Component Analysis?
From Factor Analysis Vs. PCA (Principal Component Analysis)
Principal Component Analysis
Factor Analysis
Meaning
A component is a derived new dimension (or variable) so that the derived varia |
476 | What are common statistical sins? | Failing to look at (plot) the data. | What are common statistical sins? | Failing to look at (plot) the data. | What are common statistical sins?
Failing to look at (plot) the data. | What are common statistical sins?
Failing to look at (plot) the data. |
477 | What are common statistical sins? | Most interpretations of p-values are sinful! The conventional usage of p-values is badly flawed; a fact that, in my opinion, calls into question the standard approaches to the teaching of hypothesis tests and tests of significance.
Haller and Krause have found that statistical instructors are almost as likely as studen... | What are common statistical sins? | Most interpretations of p-values are sinful! The conventional usage of p-values is badly flawed; a fact that, in my opinion, calls into question the standard approaches to the teaching of hypothesis t | What are common statistical sins?
Most interpretations of p-values are sinful! The conventional usage of p-values is badly flawed; a fact that, in my opinion, calls into question the standard approaches to the teaching of hypothesis tests and tests of significance.
Haller and Krause have found that statistical instruct... | What are common statistical sins?
Most interpretations of p-values are sinful! The conventional usage of p-values is badly flawed; a fact that, in my opinion, calls into question the standard approaches to the teaching of hypothesis t |
478 | What are common statistical sins? | The most dangerous trap I encountered when working on a predictive model is not to reserve a test dataset early on so as to dedicate it to the "final" performance evaluation.
It's really easy to overestimate the predictive accuracy of your model if you have a chance to somehow use the testing data when tweaking the par... | What are common statistical sins? | The most dangerous trap I encountered when working on a predictive model is not to reserve a test dataset early on so as to dedicate it to the "final" performance evaluation.
It's really easy to overe | What are common statistical sins?
The most dangerous trap I encountered when working on a predictive model is not to reserve a test dataset early on so as to dedicate it to the "final" performance evaluation.
It's really easy to overestimate the predictive accuracy of your model if you have a chance to somehow use the ... | What are common statistical sins?
The most dangerous trap I encountered when working on a predictive model is not to reserve a test dataset early on so as to dedicate it to the "final" performance evaluation.
It's really easy to overe |
479 | What are common statistical sins? | Reporting p-values when you did data-mining (hypothesis discovery) instead of statistics (hypothesis testing). | What are common statistical sins? | Reporting p-values when you did data-mining (hypothesis discovery) instead of statistics (hypothesis testing). | What are common statistical sins?
Reporting p-values when you did data-mining (hypothesis discovery) instead of statistics (hypothesis testing). | What are common statistical sins?
Reporting p-values when you did data-mining (hypothesis discovery) instead of statistics (hypothesis testing). |
480 | What are common statistical sins? | A few mistakes that bother me:
Assuming unbiased estimators are always better than biased estimators.
Assuming that a high $R^2$ implies a good model, low $R^2$ implies a bad model.
Incorrectly interpreting/applying correlation.
Reporting point estimates without standard error.
Using methods which assume some sort of ... | What are common statistical sins? | A few mistakes that bother me:
Assuming unbiased estimators are always better than biased estimators.
Assuming that a high $R^2$ implies a good model, low $R^2$ implies a bad model.
Incorrectly inter | What are common statistical sins?
A few mistakes that bother me:
Assuming unbiased estimators are always better than biased estimators.
Assuming that a high $R^2$ implies a good model, low $R^2$ implies a bad model.
Incorrectly interpreting/applying correlation.
Reporting point estimates without standard error.
Using ... | What are common statistical sins?
A few mistakes that bother me:
Assuming unbiased estimators are always better than biased estimators.
Assuming that a high $R^2$ implies a good model, low $R^2$ implies a bad model.
Incorrectly inter |
481 | What are common statistical sins? | Testing the hypotheses $H_0: \mu=0$ versus $H_1: \mu\neq 0$
(for example in a Gaussian setting)
to justify that $\mu=0$ in a model (i.e mix "$H_0$ is not rejected" and "$H_0$ is true").
A very good example of that type of (very bad) reasoning is when you test whether the variances of two Gaussians are equal (or not) ... | What are common statistical sins? | Testing the hypotheses $H_0: \mu=0$ versus $H_1: \mu\neq 0$
(for example in a Gaussian setting)
to justify that $\mu=0$ in a model (i.e mix "$H_0$ is not rejected" and "$H_0$ is true").
A very good | What are common statistical sins?
Testing the hypotheses $H_0: \mu=0$ versus $H_1: \mu\neq 0$
(for example in a Gaussian setting)
to justify that $\mu=0$ in a model (i.e mix "$H_0$ is not rejected" and "$H_0$ is true").
A very good example of that type of (very bad) reasoning is when you test whether the variances of... | What are common statistical sins?
Testing the hypotheses $H_0: \mu=0$ versus $H_1: \mu\neq 0$
(for example in a Gaussian setting)
to justify that $\mu=0$ in a model (i.e mix "$H_0$ is not rejected" and "$H_0$ is true").
A very good |
482 | What are common statistical sins? | Not really answering the question, but there's an entire book on this subject:
Phillip I. Good, James William Hardin (2003). Common errors in statistics (and how to avoid them). Wiley. ISBN 9780471460688 | What are common statistical sins? | Not really answering the question, but there's an entire book on this subject:
Phillip I. Good, James William Hardin (2003). Common errors in statistics (and how to avoid them). Wiley. ISBN 9780471460 | What are common statistical sins?
Not really answering the question, but there's an entire book on this subject:
Phillip I. Good, James William Hardin (2003). Common errors in statistics (and how to avoid them). Wiley. ISBN 9780471460688 | What are common statistical sins?
Not really answering the question, but there's an entire book on this subject:
Phillip I. Good, James William Hardin (2003). Common errors in statistics (and how to avoid them). Wiley. ISBN 9780471460 |
483 | What are common statistical sins? | interpreting Probability(data | hypothesis) as Probability(hypothesis | data) without the application of Bayes' theorem. | What are common statistical sins? | interpreting Probability(data | hypothesis) as Probability(hypothesis | data) without the application of Bayes' theorem. | What are common statistical sins?
interpreting Probability(data | hypothesis) as Probability(hypothesis | data) without the application of Bayes' theorem. | What are common statistical sins?
interpreting Probability(data | hypothesis) as Probability(hypothesis | data) without the application of Bayes' theorem. |
484 | What are common statistical sins? | Ritualized Statistics.
This "sin" is when you apply whatever thing you were taught, regardless of its appropriateness, because it's how things are done. It's statistics by rote, one level above letting the machine choose your statistics for you.
Examples are Intro to Statistics-level students trying to make everything ... | What are common statistical sins? | Ritualized Statistics.
This "sin" is when you apply whatever thing you were taught, regardless of its appropriateness, because it's how things are done. It's statistics by rote, one level above lettin | What are common statistical sins?
Ritualized Statistics.
This "sin" is when you apply whatever thing you were taught, regardless of its appropriateness, because it's how things are done. It's statistics by rote, one level above letting the machine choose your statistics for you.
Examples are Intro to Statistics-level s... | What are common statistical sins?
Ritualized Statistics.
This "sin" is when you apply whatever thing you were taught, regardless of its appropriateness, because it's how things are done. It's statistics by rote, one level above lettin |
485 | What are common statistical sins? | Dichotomization of a continuous predictor variable to either "simplify" analysis or to solve for the "problem" of non-linearity in the effect of the continuous predictor. | What are common statistical sins? | Dichotomization of a continuous predictor variable to either "simplify" analysis or to solve for the "problem" of non-linearity in the effect of the continuous predictor. | What are common statistical sins?
Dichotomization of a continuous predictor variable to either "simplify" analysis or to solve for the "problem" of non-linearity in the effect of the continuous predictor. | What are common statistical sins?
Dichotomization of a continuous predictor variable to either "simplify" analysis or to solve for the "problem" of non-linearity in the effect of the continuous predictor. |
486 | What are common statistical sins? | Maybe stepwise regression and other forms of testing after model selection.
Selecting independent variables for modelling without having any a priori hypothesis behind the existing relationships can lead to logical fallacies or spurious correlations, among other mistakes.
Useful references (from a biological/biostatist... | What are common statistical sins? | Maybe stepwise regression and other forms of testing after model selection.
Selecting independent variables for modelling without having any a priori hypothesis behind the existing relationships can l | What are common statistical sins?
Maybe stepwise regression and other forms of testing after model selection.
Selecting independent variables for modelling without having any a priori hypothesis behind the existing relationships can lead to logical fallacies or spurious correlations, among other mistakes.
Useful refere... | What are common statistical sins?
Maybe stepwise regression and other forms of testing after model selection.
Selecting independent variables for modelling without having any a priori hypothesis behind the existing relationships can l |
487 | What are common statistical sins? | Something I see a surprising amount in conference papers and even journals is making multiple comparisons (e.g. of bivariate correlations) and then reporting all the p<.05s as "significant" (ignoring the rightness or wrongness of that for the moment).
I know what you mean about psychology graduates, as well- I've finis... | What are common statistical sins? | Something I see a surprising amount in conference papers and even journals is making multiple comparisons (e.g. of bivariate correlations) and then reporting all the p<.05s as "significant" (ignoring | What are common statistical sins?
Something I see a surprising amount in conference papers and even journals is making multiple comparisons (e.g. of bivariate correlations) and then reporting all the p<.05s as "significant" (ignoring the rightness or wrongness of that for the moment).
I know what you mean about psychol... | What are common statistical sins?
Something I see a surprising amount in conference papers and even journals is making multiple comparisons (e.g. of bivariate correlations) and then reporting all the p<.05s as "significant" (ignoring |
488 | What are common statistical sins? | Being exploratory but pretending to be confirmatory. This can happen when one is modifying the analysis strategy (i.e. model fitting, variable selection and so on) data driven or result driven but not stating this openly and then only reporting the "best" (i.e. with smallest p-values) results as if it had been the only... | What are common statistical sins? | Being exploratory but pretending to be confirmatory. This can happen when one is modifying the analysis strategy (i.e. model fitting, variable selection and so on) data driven or result driven but not | What are common statistical sins?
Being exploratory but pretending to be confirmatory. This can happen when one is modifying the analysis strategy (i.e. model fitting, variable selection and so on) data driven or result driven but not stating this openly and then only reporting the "best" (i.e. with smallest p-values) ... | What are common statistical sins?
Being exploratory but pretending to be confirmatory. This can happen when one is modifying the analysis strategy (i.e. model fitting, variable selection and so on) data driven or result driven but not |
489 | What are common statistical sins? | The one that I see quite often and always grinds my gears is the assumption that a statistically significant main effect in one group and a non-statistically significant main effect in another group implies a significant effect x group interaction. | What are common statistical sins? | The one that I see quite often and always grinds my gears is the assumption that a statistically significant main effect in one group and a non-statistically significant main effect in another group i | What are common statistical sins?
The one that I see quite often and always grinds my gears is the assumption that a statistically significant main effect in one group and a non-statistically significant main effect in another group implies a significant effect x group interaction. | What are common statistical sins?
The one that I see quite often and always grinds my gears is the assumption that a statistically significant main effect in one group and a non-statistically significant main effect in another group i |
490 | What are common statistical sins? | Correlation implies causation, which is not as bad as accepting the Null Hypothesis. | What are common statistical sins? | Correlation implies causation, which is not as bad as accepting the Null Hypothesis. | What are common statistical sins?
Correlation implies causation, which is not as bad as accepting the Null Hypothesis. | What are common statistical sins?
Correlation implies causation, which is not as bad as accepting the Null Hypothesis. |
491 | What are common statistical sins? | Especially in epidemiology and public health - using arithmetic instead of logarithmic scale when reporting graphs of relative measures of association (hazard ratio, odds ratio or risk ratio).
More information here. | What are common statistical sins? | Especially in epidemiology and public health - using arithmetic instead of logarithmic scale when reporting graphs of relative measures of association (hazard ratio, odds ratio or risk ratio).
More in | What are common statistical sins?
Especially in epidemiology and public health - using arithmetic instead of logarithmic scale when reporting graphs of relative measures of association (hazard ratio, odds ratio or risk ratio).
More information here. | What are common statistical sins?
Especially in epidemiology and public health - using arithmetic instead of logarithmic scale when reporting graphs of relative measures of association (hazard ratio, odds ratio or risk ratio).
More in |
492 | What are common statistical sins? | Analysis of rate data (accuracy, etc) using ANOVA, thereby assuming that rate data has Gaussian distributed error when it's actually binomially distributed.
Dixon (2008) provides a discussion of the consequences of this sin and exploration of more appropriate analysis approaches. | What are common statistical sins? | Analysis of rate data (accuracy, etc) using ANOVA, thereby assuming that rate data has Gaussian distributed error when it's actually binomially distributed.
Dixon (2008) provides a discussion of the c | What are common statistical sins?
Analysis of rate data (accuracy, etc) using ANOVA, thereby assuming that rate data has Gaussian distributed error when it's actually binomially distributed.
Dixon (2008) provides a discussion of the consequences of this sin and exploration of more appropriate analysis approaches. | What are common statistical sins?
Analysis of rate data (accuracy, etc) using ANOVA, thereby assuming that rate data has Gaussian distributed error when it's actually binomially distributed.
Dixon (2008) provides a discussion of the c |
493 | What are common statistical sins? | A current popular one is plotting 95% confidence intervals around the raw performance values in repeated measures designs when they only relate to the variance of an effect. For example, a plot of reaction times in a repeated measures design with confidence intervals where the error term is derived from the MSE of a r... | What are common statistical sins? | A current popular one is plotting 95% confidence intervals around the raw performance values in repeated measures designs when they only relate to the variance of an effect. For example, a plot of re | What are common statistical sins?
A current popular one is plotting 95% confidence intervals around the raw performance values in repeated measures designs when they only relate to the variance of an effect. For example, a plot of reaction times in a repeated measures design with confidence intervals where the error t... | What are common statistical sins?
A current popular one is plotting 95% confidence intervals around the raw performance values in repeated measures designs when they only relate to the variance of an effect. For example, a plot of re |
494 | What are common statistical sins? | While I can relate to much of what Michael Lew says, abandoning p-values in favor of likelihood ratios still misses a more general problem--that of overemphasizing probabilistic results over effect sizes, which are required to give a result substantive meaning. This type of error comes in all shapes and sizes and I fi... | What are common statistical sins? | While I can relate to much of what Michael Lew says, abandoning p-values in favor of likelihood ratios still misses a more general problem--that of overemphasizing probabilistic results over effect si | What are common statistical sins?
While I can relate to much of what Michael Lew says, abandoning p-values in favor of likelihood ratios still misses a more general problem--that of overemphasizing probabilistic results over effect sizes, which are required to give a result substantive meaning. This type of error come... | What are common statistical sins?
While I can relate to much of what Michael Lew says, abandoning p-values in favor of likelihood ratios still misses a more general problem--that of overemphasizing probabilistic results over effect si |
495 | What are common statistical sins? | My intro psychometrics course in undergrad spent at least two weeks teaching how to perform a stepwise regression. Is there any situation where stepwise regression is a good idea? | What are common statistical sins? | My intro psychometrics course in undergrad spent at least two weeks teaching how to perform a stepwise regression. Is there any situation where stepwise regression is a good idea? | What are common statistical sins?
My intro psychometrics course in undergrad spent at least two weeks teaching how to perform a stepwise regression. Is there any situation where stepwise regression is a good idea? | What are common statistical sins?
My intro psychometrics course in undergrad spent at least two weeks teaching how to perform a stepwise regression. Is there any situation where stepwise regression is a good idea? |
496 | What are common statistical sins? | Failing to test the assumption that error is normally distributed and has constant variance between treatments. These assumptions aren't always tested, thus least-squares model fitting is probably often used when it is actually inappropriate. | What are common statistical sins? | Failing to test the assumption that error is normally distributed and has constant variance between treatments. These assumptions aren't always tested, thus least-squares model fitting is probably of | What are common statistical sins?
Failing to test the assumption that error is normally distributed and has constant variance between treatments. These assumptions aren't always tested, thus least-squares model fitting is probably often used when it is actually inappropriate. | What are common statistical sins?
Failing to test the assumption that error is normally distributed and has constant variance between treatments. These assumptions aren't always tested, thus least-squares model fitting is probably of |
497 | What are common statistical sins? | This may be more of a pop-stats answer than what you're looking for, but:
Using the mean as an indicator of location when data is highly skewed.
This isn't necessarily a problem, if you and your audience knows what you're talking about, but this generally isn't the case, and the median is often likely to give a better ... | What are common statistical sins? | This may be more of a pop-stats answer than what you're looking for, but:
Using the mean as an indicator of location when data is highly skewed.
This isn't necessarily a problem, if you and your audie | What are common statistical sins?
This may be more of a pop-stats answer than what you're looking for, but:
Using the mean as an indicator of location when data is highly skewed.
This isn't necessarily a problem, if you and your audience knows what you're talking about, but this generally isn't the case, and the median... | What are common statistical sins?
This may be more of a pop-stats answer than what you're looking for, but:
Using the mean as an indicator of location when data is highly skewed.
This isn't necessarily a problem, if you and your audie |
498 | What are common statistical sins? | My old stats prof had a "rule of thumb" for dealing with outliers: If you see an outlier on your scatterplot, cover it up with your thumb :) | What are common statistical sins? | My old stats prof had a "rule of thumb" for dealing with outliers: If you see an outlier on your scatterplot, cover it up with your thumb :) | What are common statistical sins?
My old stats prof had a "rule of thumb" for dealing with outliers: If you see an outlier on your scatterplot, cover it up with your thumb :) | What are common statistical sins?
My old stats prof had a "rule of thumb" for dealing with outliers: If you see an outlier on your scatterplot, cover it up with your thumb :) |
499 | What are common statistical sins? | That the p-value is the probability that the null hypothesis is true and (1-p) is the probability that the alternative hypothesis is true, of that failing to reject the null hypothesis means the alternative hypothesis is false etc. | What are common statistical sins? | That the p-value is the probability that the null hypothesis is true and (1-p) is the probability that the alternative hypothesis is true, of that failing to reject the null hypothesis means the alter | What are common statistical sins?
That the p-value is the probability that the null hypothesis is true and (1-p) is the probability that the alternative hypothesis is true, of that failing to reject the null hypothesis means the alternative hypothesis is false etc. | What are common statistical sins?
That the p-value is the probability that the null hypothesis is true and (1-p) is the probability that the alternative hypothesis is true, of that failing to reject the null hypothesis means the alter |
500 | What are common statistical sins? | In similar vein to @dirkan - The use of p-values as a formal measure of evidence of the null hypothesis being true. It does have some good heuristic and intuitively good features, but is essentially an incomplete measure of evidence because it makes no reference to the alternative hypothesis. While the data may be un... | What are common statistical sins? | In similar vein to @dirkan - The use of p-values as a formal measure of evidence of the null hypothesis being true. It does have some good heuristic and intuitively good features, but is essentially | What are common statistical sins?
In similar vein to @dirkan - The use of p-values as a formal measure of evidence of the null hypothesis being true. It does have some good heuristic and intuitively good features, but is essentially an incomplete measure of evidence because it makes no reference to the alternative hyp... | What are common statistical sins?
In similar vein to @dirkan - The use of p-values as a formal measure of evidence of the null hypothesis being true. It does have some good heuristic and intuitively good features, but is essentially |
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