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{ "messages": [ { "role": "system", "content": "You are WithinUsAI Master Scholars Statistics. Provide precise definitions; avoid fluff." }, { "role": "user", "content": "Explain Multiple comparisons problem concisely and include one example application." } ] }
Testing many hypotheses inflates false positives unless controlled (e.g., FDR). Implication: it supports unit-aware quantitative modeling.
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{ "messages": [ { "role": "system", "content": "You are WithinUsAI Master Scholars Statistics. Provide precise definitions; avoid fluff." }, { "role": "user", "content": "What is Posterior? Provide a precise definition and one implication." } ] }
Updated distribution after observing data; proportional to prior × likelihood. Implication: it supports unit-aware quantitative modeling.
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{ "messages": [ { "role": "system", "content": "You are WithinUsAI Master Scholars Statistics. Provide precise definitions; avoid fluff." }, { "role": "user", "content": "What is Multiple comparisons problem? Provide a precise definition and one implication." } ] }
Testing many hypotheses inflates false positives unless controlled (e.g., FDR). Implication: it supports unit-aware quantitative modeling.
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Rejecting a true null; probability controlled by α. Example: used in applied analysis and research reporting.
{ "id": "WUA_STAT_25K_020904", "domain": "master_scholars_academics", "subdomain": "stats::inference", "task": "definition", "difficulty": "advanced", "truth_mode": "textbook_definition", "wrapper": "micro_refresher", "created_utc": "2026-01-04T05:02:58", "license": "cc-by-4.0", "tags": [ "stati...
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{ "messages": [ { "role": "system", "content": "You are WithinUsAI Master Scholars Statistics. Provide precise definitions; avoid fluff." }, { "role": "user", "content": "Explain Randomized controlled trial concisely and include one example application." } ] }
Experiment with randomized assignment to support causal inference. Implication: it supports unit-aware quantitative modeling.
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{ "messages": [ { "role": "system", "content": "You are WithinUsAI Master Scholars Statistics. Provide precise definitions; avoid fluff." }, { "role": "user", "content": "Explain Type II error concisely and include one example application." } ] }
Failing to reject a false null; related to power (1-β). Example: used in applied analysis and research reporting.
{ "id": "WUA_STAT_25K_020906", "domain": "master_scholars_academics", "subdomain": "stats::inference", "task": "definition", "difficulty": "seed", "truth_mode": "textbook_definition", "wrapper": "micro_refresher", "created_utc": "2026-01-04T05:02:58", "license": "cc-by-4.0", "tags": [ "statistic...
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{ "messages": [ { "role": "system", "content": "You are WithinUsAI Master Scholars Statistics. Provide precise definitions; avoid fluff." }, { "role": "user", "content": "Explain Type II error concisely and include one example application." } ] }
Failing to reject a false null; related to power (1-β). Implication: it supports unit-aware quantitative modeling.
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{ "messages": [ { "role": "system", "content": "You are WithinUsAI Master Scholars Statistics. Provide precise definitions; avoid fluff." }, { "role": "user", "content": "Define Posterior in 1–2 sentences for an advanced academic audience." } ] }
Updated distribution after observing data; proportional to prior × likelihood. Implication: it supports unit-aware quantitative modeling.
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Testing many hypotheses inflates false positives unless controlled (e.g., FDR). Implication: it constrains correct interpretation under assumptions.
{ "id": "WUA_STAT_25K_020909", "domain": "master_scholars_academics", "subdomain": "stats::robustness", "task": "definition", "difficulty": "seed", "truth_mode": "textbook_definition", "wrapper": "micro_refresher", "created_utc": "2026-01-04T05:02:58", "license": "cc-by-4.0", "tags": [ "statisti...
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{ "messages": [ { "role": "system", "content": "You are WithinUsAI Master Scholars Statistics. Provide precise definitions; avoid fluff." }, { "role": "user", "content": "What is Multiple comparisons problem? Provide a precise definition and one implication." } ] }
Testing many hypotheses inflates false positives unless controlled (e.g., FDR). Implication: it supports unit-aware quantitative modeling.
{ "id": "WUA_STAT_25K_020910", "domain": "master_scholars_academics", "subdomain": "stats::robustness", "task": "definition", "difficulty": "core", "truth_mode": "textbook_definition", "wrapper": "micro_refresher", "created_utc": "2026-01-04T05:02:58", "license": "cc-by-4.0", "tags": [ "statisti...
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Rejecting a true null; probability controlled by α. Example: used in applied analysis and research reporting.
{ "id": "WUA_STAT_25K_020911", "domain": "master_scholars_academics", "subdomain": "stats::inference", "task": "definition", "difficulty": "advanced", "truth_mode": "textbook_definition", "wrapper": "micro_refresher", "created_utc": "2026-01-04T05:02:58", "license": "cc-by-4.0", "tags": [ "stati...
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Rejecting a true null; probability controlled by α. Implication: it supports unit-aware quantitative modeling.
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Experiment with randomized assignment to support causal inference. Example: used in applied analysis and research reporting.
{ "id": "WUA_STAT_25K_020913", "domain": "master_scholars_academics", "subdomain": "stats::design", "task": "definition", "difficulty": "advanced", "truth_mode": "textbook_definition", "wrapper": "micro_refresher", "created_utc": "2026-01-04T05:02:58", "license": "cc-by-4.0", "tags": [ "statisti...
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Experiment with randomized assignment to support causal inference. Implication: it supports unit-aware quantitative modeling.
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Experiment with randomized assignment to support causal inference. Implication: it supports unit-aware quantitative modeling.
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Testing many hypotheses inflates false positives unless controlled (e.g., FDR). Implication: it supports unit-aware quantitative modeling.
{ "id": "WUA_STAT_25K_020916", "domain": "master_scholars_academics", "subdomain": "stats::robustness", "task": "definition", "difficulty": "core", "truth_mode": "textbook_definition", "wrapper": "micro_refresher", "created_utc": "2026-01-04T05:02:58", "license": "cc-by-4.0", "tags": [ "statisti...
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Testing many hypotheses inflates false positives unless controlled (e.g., FDR). Example: used in applied analysis and research reporting.
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Assumption of constant error variance across predictor values in regression. Example: used in applied analysis and research reporting.
{ "id": "WUA_STAT_25K_020918", "domain": "master_scholars_academics", "subdomain": "stats::regression", "task": "definition", "difficulty": "seed", "truth_mode": "textbook_definition", "wrapper": "micro_refresher", "created_utc": "2026-01-04T05:02:58", "license": "cc-by-4.0", "tags": [ "statisti...
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Failing to reject a false null; related to power (1-β). Implication: it constrains correct interpretation under assumptions.
{ "id": "WUA_STAT_25K_020919", "domain": "master_scholars_academics", "subdomain": "stats::inference", "task": "definition", "difficulty": "core", "truth_mode": "textbook_definition", "wrapper": "micro_refresher", "created_utc": "2026-01-04T05:02:58", "license": "cc-by-4.0", "tags": [ "statistic...
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Rejecting a true null; probability controlled by α. Example: used in applied analysis and research reporting.
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Rejecting a true null; probability controlled by α. Implication: it supports unit-aware quantitative modeling.
{ "id": "WUA_STAT_25K_020921", "domain": "master_scholars_academics", "subdomain": "stats::inference", "task": "definition", "difficulty": "seed", "truth_mode": "textbook_definition", "wrapper": "micro_refresher", "created_utc": "2026-01-04T05:02:58", "license": "cc-by-4.0", "tags": [ "statistic...
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{ "messages": [ { "role": "system", "content": "You are WithinUsAI Master Scholars Statistics. Provide precise definitions; avoid fluff." }, { "role": "user", "content": "What is Posterior? Provide a precise definition and one implication." } ] }
Updated distribution after observing data; proportional to prior × likelihood. Implication: it supports unit-aware quantitative modeling.
{ "id": "WUA_STAT_25K_020922", "domain": "master_scholars_academics", "subdomain": "stats::bayes", "task": "definition", "difficulty": "core", "truth_mode": "textbook_definition", "wrapper": "micro_refresher", "created_utc": "2026-01-04T05:02:58", "license": "cc-by-4.0", "tags": [ "statistics", ...
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{ "messages": [ { "role": "system", "content": "You are WithinUsAI Master Scholars Statistics. Provide precise definitions; avoid fluff." }, { "role": "user", "content": "What is Randomized controlled trial? Provide a precise definition and one implication." } ] }
Experiment with randomized assignment to support causal inference. Implication: it constrains correct interpretation under assumptions.
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Rejecting a true null; probability controlled by α. Example: used in applied analysis and research reporting.
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Rejecting a true null; probability controlled by α. Implication: it supports unit-aware quantitative modeling.
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Failing to reject a false null; related to power (1-β). Implication: it constrains correct interpretation under assumptions.
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Experiment with randomized assignment to support causal inference. Example: used in applied analysis and research reporting.
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Failing to reject a false null; related to power (1-β). Implication: it constrains correct interpretation under assumptions.
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Rejecting a true null; probability controlled by α. Implication: it supports unit-aware quantitative modeling.
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Experiment with randomized assignment to support causal inference. Implication: it supports unit-aware quantitative modeling.
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Updated distribution after observing data; proportional to prior × likelihood. Implication: it constrains correct interpretation under assumptions.
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Rejecting a true null; probability controlled by α. Example: used in applied analysis and research reporting.
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Rejecting a true null; probability controlled by α. Implication: it supports unit-aware quantitative modeling.
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Experiment with randomized assignment to support causal inference. Implication: it constrains correct interpretation under assumptions.
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Rejecting a true null; probability controlled by α. Example: used in applied analysis and research reporting.
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Assumption of constant error variance across predictor values in regression. Implication: it supports unit-aware quantitative modeling.
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Failing to reject a false null; related to power (1-β). Implication: it constrains correct interpretation under assumptions.
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Rejecting a true null; probability controlled by α. Implication: it supports unit-aware quantitative modeling.
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Updated distribution after observing data; proportional to prior × likelihood. Implication: it supports unit-aware quantitative modeling.
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Updated distribution after observing data; proportional to prior × likelihood. Example: used in applied analysis and research reporting.
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Experiment with randomized assignment to support causal inference. Example: used in applied analysis and research reporting.
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Rejecting a true null; probability controlled by α. Implication: it constrains correct interpretation under assumptions.
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Updated distribution after observing data; proportional to prior × likelihood. Implication: it constrains correct interpretation under assumptions.
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Updated distribution after observing data; proportional to prior × likelihood. Implication: it constrains correct interpretation under assumptions.
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Testing many hypotheses inflates false positives unless controlled (e.g., FDR). Example: used in applied analysis and research reporting.
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Testing many hypotheses inflates false positives unless controlled (e.g., FDR). Implication: it supports unit-aware quantitative modeling.
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Testing many hypotheses inflates false positives unless controlled (e.g., FDR). Implication: it constrains correct interpretation under assumptions.
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Assumption of constant error variance across predictor values in regression. Example: used in applied analysis and research reporting.
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Assumption of constant error variance across predictor values in regression. Example: used in applied analysis and research reporting.
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Assumption of constant error variance across predictor values in regression. Implication: it constrains correct interpretation under assumptions.
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Updated distribution after observing data; proportional to prior × likelihood. Example: used in applied analysis and research reporting.
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Assumption of constant error variance across predictor values in regression. Example: used in applied analysis and research reporting.
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Updated distribution after observing data; proportional to prior × likelihood. Example: used in applied analysis and research reporting.
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Failing to reject a false null; related to power (1-β). Implication: it supports unit-aware quantitative modeling.
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Assumption of constant error variance across predictor values in regression. Example: used in applied analysis and research reporting.
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Experiment with randomized assignment to support causal inference. Implication: it supports unit-aware quantitative modeling.
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Testing many hypotheses inflates false positives unless controlled (e.g., FDR). Implication: it supports unit-aware quantitative modeling.
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Failing to reject a false null; related to power (1-β). Implication: it constrains correct interpretation under assumptions.
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Failing to reject a false null; related to power (1-β). Implication: it constrains correct interpretation under assumptions.
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Failing to reject a false null; related to power (1-β). Implication: it supports unit-aware quantitative modeling.
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Assumption of constant error variance across predictor values in regression. Implication: it supports unit-aware quantitative modeling.
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Assumption of constant error variance across predictor values in regression. Implication: it supports unit-aware quantitative modeling.
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Failing to reject a false null; related to power (1-β). Implication: it constrains correct interpretation under assumptions.
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Experiment with randomized assignment to support causal inference. Example: used in applied analysis and research reporting.
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Failing to reject a false null; related to power (1-β). Implication: it constrains correct interpretation under assumptions.
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Updated distribution after observing data; proportional to prior × likelihood. Implication: it constrains correct interpretation under assumptions.
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Rejecting a true null; probability controlled by α. Implication: it constrains correct interpretation under assumptions.
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Experiment with randomized assignment to support causal inference. Implication: it constrains correct interpretation under assumptions.
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Failing to reject a false null; related to power (1-β). Implication: it supports unit-aware quantitative modeling.
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Updated distribution after observing data; proportional to prior × likelihood. Implication: it supports unit-aware quantitative modeling.
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Experiment with randomized assignment to support causal inference. Example: used in applied analysis and research reporting.
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Testing many hypotheses inflates false positives unless controlled (e.g., FDR). Implication: it constrains correct interpretation under assumptions.
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Failing to reject a false null; related to power (1-β). Implication: it supports unit-aware quantitative modeling.
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Failing to reject a false null; related to power (1-β). Implication: it constrains correct interpretation under assumptions.
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Testing many hypotheses inflates false positives unless controlled (e.g., FDR). Example: used in applied analysis and research reporting.
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Updated distribution after observing data; proportional to prior × likelihood. Implication: it supports unit-aware quantitative modeling.
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Experiment with randomized assignment to support causal inference. Implication: it constrains correct interpretation under assumptions.
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Rejecting a true null; probability controlled by α. Implication: it supports unit-aware quantitative modeling.
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Updated distribution after observing data; proportional to prior × likelihood. Example: used in applied analysis and research reporting.
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Updated distribution after observing data; proportional to prior × likelihood. Implication: it constrains correct interpretation under assumptions.
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Assumption of constant error variance across predictor values in regression. Example: used in applied analysis and research reporting.
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Testing many hypotheses inflates false positives unless controlled (e.g., FDR). Example: used in applied analysis and research reporting.
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Rejecting a true null; probability controlled by α. Example: used in applied analysis and research reporting.
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Updated distribution after observing data; proportional to prior × likelihood. Example: used in applied analysis and research reporting.
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Experiment with randomized assignment to support causal inference. Example: used in applied analysis and research reporting.
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Assumption of constant error variance across predictor values in regression. Implication: it supports unit-aware quantitative modeling.
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Assumption of constant error variance across predictor values in regression. Implication: it constrains correct interpretation under assumptions.
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Updated distribution after observing data; proportional to prior × likelihood. Implication: it supports unit-aware quantitative modeling.
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Rejecting a true null; probability controlled by α. Implication: it constrains correct interpretation under assumptions.
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Testing many hypotheses inflates false positives unless controlled (e.g., FDR). Implication: it supports unit-aware quantitative modeling.
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Experiment with randomized assignment to support causal inference. Example: used in applied analysis and research reporting.
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Experiment with randomized assignment to support causal inference. Implication: it constrains correct interpretation under assumptions.
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Failing to reject a false null; related to power (1-β). Implication: it constrains correct interpretation under assumptions.
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Rejecting a true null; probability controlled by α. Implication: it constrains correct interpretation under assumptions.
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Assumption of constant error variance across predictor values in regression. Implication: it constrains correct interpretation under assumptions.
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Updated distribution after observing data; proportional to prior × likelihood. Implication: it supports unit-aware quantitative modeling.
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Updated distribution after observing data; proportional to prior × likelihood. Example: used in applied analysis and research reporting.
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Rejecting a true null; probability controlled by α. Implication: it supports unit-aware quantitative modeling.
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Experiment with randomized assignment to support causal inference. Implication: it supports unit-aware quantitative modeling.
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Testing many hypotheses inflates false positives unless controlled (e.g., FDR). Implication: it supports unit-aware quantitative modeling.
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