Replication data for: Testing What Matters (If You Must Test at All): A Context-Driven Approach to Substantive and Statistical Significance
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'For over a half-century, various fields in the behavioral and social sciences have debated the appropriateness of null hypothesis significance testing (NHST) in the presentation of research results. A long list of criticisms has fueled the so-called significance testing controversy. The conventional NHST framework encourages researchers to devote excessive attention to statistical significance while underemphasizing practical (scientific, substantive, social, political, etc.) significance. I introduce a simple, intuitive approach that grounds testing in subject-area expertise, balancing the dual concerns of detectability and importance. The proposed practical and statistical significance test allows the social scientist to focus upon real-world significance, taking into account both sampling error and an assessment of what parameter val' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Empirical methods","Hypothesis testing","Methodology","NHST","Statistical inference","Statistical significance testing"]} <|eot_id|>
 Here is the Description of a dataset. Description:'For over a half-century, various fields in the behavioral and social sciences have debated the appropriateness of null hypothesis significance testing (NHST) in the presentation of research results. A long list of criticisms has fueled the so-called significance testing controversy. The conventional NHST framework encourages researchers to devote excessive attention to statistical significance while underemphasizing practical (scientific, substantive, social, political, etc.) significance. I introduce a simple, intuitive approach that grounds testing in subject-area expertise, balancing the dual concerns of detectability and importance. The proposed practical and statistical significance test allows the social scientist to focus upon real-world significance, taking into account both sampling error and an assessment of what parameter val' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
For over a half-century, various fields in the behavioral and social sciences have debated the appropriateness of null hypothesis significance testing (NHST) in the presentation of research results. A long list of criticisms has fueled the so-called significance testing controversy. The conventional NHST framework encourages researchers to devote excessive attention to statistical significance while underemphasizing practical (scientific, substantive, social, political, etc.) significance. I introduce a simple, intuitive approach that grounds testing in subject-area expertise, balancing the dual concerns of detectability and importance. The proposed practical and statistical significance test allows the social scientist to focus upon real-world significance, taking into account both sampling error and an assessment of what parameter val
Empirical methods; Hypothesis testing; Methodology; NHST; Statistical inference; Statistical significance testing