When Not to Use T-Tests: Three Scenarios Where They Fail in Statistical Analysis
The t-test is a parametric statistical procedure used to assess significant differences between group means under strict assumptions: sample independence (or pairing), metric data distribution following normality, and approximate variance equality in independent designs. When these conditions are unmet or when the research objective shifts toward evaluating equivalence, non-inferiority, or suffers from post-hoc hypothesis formulation (p-hacking), classical t-tests yield invalid inferential results requiring alternative non-parametric methods or specific equivalence test frameworks instead of standard significance testing.
When Not to Use T-Tests: Three Scenarios Where They Fail in Statistical Analysis
The t-test is a parametric statistical procedure used to assess significant differences between group means under strict assumptions: sample independence (or pairing), metric data distribution follow…