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week4_running.csv

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+TERM,CONTEXT
+Help with a code bug in R,For this option you should prompt the user to copy and paste the relevant code and the error as it appears in the console. You ARE NOT allowed to directly solve the bug for them and give them fixed code to copy and paste. Instead you will socratically ask them guiding questions that logically guides them to solving it for themselves. Use palmer penguins for your examples. The issue will be in relation to RStudio R and Rmd files.
+Testable Hypothesis,A hypothesis that can be evaluated through empirical evidence and experimentation.
+Falsifiable Hypothesis,A hypothesis that allows for the possibility of being disproven by data.
+Prediction vs. Hypothesis,A prediction is a specific expected outcome of an experiment based on a hypothesis.
+Environmental Factors in Foraging Behavior of white-footed mice,Includes habitat type - availability of refuge - predator presence - moonlight - and temporal effects.
+Scientific Research Question,A specific and clear question that guides an investigation and is based on observations. Include information that is specific to the population experimental design and collected data
+Background Research in Science,The step where existing literature and prior knowledge are reviewed to refine research questions.
+Keefe’s Hypothesis Trick,A strategy for writing clear hypotheses by imagining the axes and predicted outcomes in a graph. For example - X axis is positively correlated with Y axis
+Alternative Hypothesis (Hₐ),A statement suggesting that there is a meaningful relationship or effect between variables.
+Null Hypothesis (H₀),The assumption that there is no effect or difference unless proven otherwise. It is easier to disprove a null than prove an alternative hypothesis
+Parametric statistical tests,
+Three Assumptions of most parametric stat tests, Normality Homoscedasticity (equal variance between groups) and Data Independence.
+skewness, of a distribution; skewed left or right
+kurtosis, of a distribution; platykurtic and leptokurtic
+Common data transformations, square root; natural log; log10; multiplicative inverse; or rank transformation as a last resort
+How to choose the right transformation?, Choose the transformation that best meets normality assumptions while preserving interpretability. 
+back transformation, why is this necessary in stat after a transformation
+The Kolmogorov-Smirnov (KS) test, 
+The Shapiro-Wilks test,
+The D'Agostino's K^2 test,
+How robust is the assumption of normality?, very unless the sample size is low or the data is highly skewed; Do non-parametric test and parametric tests and see if the results agree.
+Continuous probability distributions common in biological systems,
+Discontinuous probability distributions common in biological systems,
+distributions,Within the context of statistics and populations
+hypothesis testing,Within the context of statistics and populations
+null hypothesis,Within the context of statistics and populations
+The wisdom of crows,Within the context of statistics and populations
+Stabilizing selection,an example of a mechanism that creates normal distributions in nature.
+The Central Limit Theorem,Within the context of statistics and populations
+Standard error,Standard error of the mean tells you how accurate your estimate of the real mean it is likely to be.
+Confidence intervals,
+The difference between standard error and standard deviation,
+Data Visualization in Biology,Questions to ask yourself: How does my data vary? Are variables correlated? Are there outliers?
+The Palmer Penguins dataset,the primary example dataset used in class. Assume students do know know the names and data types of the columns so they should be frequently provided for context.
+bar plot,
+scatterplot,
+line graph,
+histogram,
+box plot,
+violin plot,
+heat map,
+pie charts,this is an awful choice for many reasons.
+O-ring failure on the Challenger and bad data visualization,
+ggplot2 and the grammar of graphics,
+descriptive statistics,
+robust,this is in reference to the robustness of a model or descriptive statistic.
+centrality and variation in statistics,
+interquartile range,
+standard deviation,
+variance,this is in reference to the descriptive statistic.
+sum of squares,
+Anscombe’s quartet,
+range,this is in reference to the descriptive statistic.
+When should you look at your data?,Early and often with data visualization. Science is an iterative process where the initial experimental plans often won't work because of some unknown patterns in your population. Examining your sample visually can help show those to you - like outliers or weird distributions.
+Science,Science is the pursuit and application of knowledge and understanding of the natural and social world following a systematic methodology based on evidence.
+PPDAC - Problem,
+PPDAC - Plan,
+PPDAC - Data,
+PPDAC - Analysis,
+PPDAC - Conclusion,This step cycles back to 'PPDAC - Problem' because science is an iterative process that creates new questions and directions.
+Biological biases,
+Cognitive biases,
+Confirmation bias,
+Availability heuristic,
+Anchoring bias,
+Dunning-Kruger effect,
+Why are statistical and programming knowledge useful for a career in biology?,1. Biological data is messy and complex. 2. Biology uses BIG data. 3. These skills save lives. 4. These skills will help your career.
+Big data in biology,
+Name a disease that is influenced by many different genetic and environmental factors, 
+a priori hypotheses,
+Which natural process is most similar to machine learning?," ""evolution by natural selection"""
+How will careers in biology be affected by generative AI?," ""The barriers to learning new skills are falling. The need to learn programming and data analysis is greater now than it was in 2020! Some jobs will be automated. Those with the rarest and most useful combination of skills will be sought after."""
+Data types - continuous/numerical,
+Data types - count/integer,
+Data types - ordinal,
+Data types - categorical,
+Data types - binomial,
+Null data,Data missing in a dataset. It is important to appropriately deal with this data depending on the nature of the statistical analysis and experimental design.
+tidy data,
+R programming - objects,
+R programming - functions,
+R programming - Rmd file format,
+RStudio,
+print() in R,