The article "Equivalence Testing for Psychological Research: A Tutorial" by Daniël Lakens, Anne M. Scheel, and Peder M. Isager provides a comprehensive guide to equivalence testing in psychological research. Equivalence testing is a statistical method used to determine whether an observed effect is too small to be considered meaningful, given the existence of a true effect at least as large as the smallest effect size of interest (SESOI). The authors explain the concept of equivalence testing, its application, and its benefits in psychological research. Key points include: - **Null Hypothesis Significance Testing (NHST)**: Traditional NHST tests whether an effect is zero, but equivalence testing can test whether an effect is at least as small as a specified threshold. - **Two One-Sided Tests (TOST)**: Equivalence testing involves performing two one-sided tests to determine if the observed effect is within the equivalence bounds. - **Justifying the SESOI**: The SESOI should be justified based on theoretical predictions, cost-benefit analyses, or previous studies. The authors provide several methods for justifying the SESOI. - **Examples**: The article includes five detailed examples of equivalence testing, each demonstrating different scenarios and how to perform and report equivalence tests. - **Software**: The authors provide R code and a spreadsheet to facilitate the implementation of equivalence tests, using the TOSTER package. The article emphasizes the importance of specifying the SESOI before data collection and the need for transparency in justifying the choice of equivalence bounds. Equivalence testing is recommended as a complementary tool to NHST to enhance the falsifiability of psychological theories and improve the robustness of research findings.The article "Equivalence Testing for Psychological Research: A Tutorial" by Daniël Lakens, Anne M. Scheel, and Peder M. Isager provides a comprehensive guide to equivalence testing in psychological research. Equivalence testing is a statistical method used to determine whether an observed effect is too small to be considered meaningful, given the existence of a true effect at least as large as the smallest effect size of interest (SESOI). The authors explain the concept of equivalence testing, its application, and its benefits in psychological research. Key points include: - **Null Hypothesis Significance Testing (NHST)**: Traditional NHST tests whether an effect is zero, but equivalence testing can test whether an effect is at least as small as a specified threshold. - **Two One-Sided Tests (TOST)**: Equivalence testing involves performing two one-sided tests to determine if the observed effect is within the equivalence bounds. - **Justifying the SESOI**: The SESOI should be justified based on theoretical predictions, cost-benefit analyses, or previous studies. The authors provide several methods for justifying the SESOI. - **Examples**: The article includes five detailed examples of equivalence testing, each demonstrating different scenarios and how to perform and report equivalence tests. - **Software**: The authors provide R code and a spreadsheet to facilitate the implementation of equivalence tests, using the TOSTER package. The article emphasizes the importance of specifying the SESOI before data collection and the need for transparency in justifying the choice of equivalence bounds. Equivalence testing is recommended as a complementary tool to NHST to enhance the falsifiability of psychological theories and improve the robustness of research findings.