hypothesis testing #biostatistics #shorts

Always report an effect size and a confidence (or credible) interval, not just a $p$-value. Statistical significance $\neq$ practical importance. Multiple testing inflates the Type I error rate → corrections (Bonferroni, FDR, etc.) or hierarchical Bayesian models are needed. Pre-specify the analysis plan when possible (especially in confirmatory research). Check assumptions (normality, independence, equal variances, etc.) or use robust / non-parametric / Bayesian alternatives. #hypothesistesting #biostatistics #statistics #dataanalysis #researchmethods #scientificmethod #statisticalsignificance #quantitativeresearch #evidencesynthesis #academicresearch