statistical-analysis

To choose the right statistical test and write an APA-style results section, you match tests to your variables and distribution, run assumption checks, calculate effect sizes with confidence intervals, and generate a publication-ready summary.

Checking statistical assumptions requires running systematic diagnostics for normality, variance homogeneity, outliers, and linearity to determine if you need corrections like Welch’s or non-parametric alternatives before running a t-test or ANOVA.

You calculate effect sizes with confidence intervals for academic research reporting by applying the selected statistical test, extracting the magnitude metrics, and formatting them into an APA-compliant diagnostic interpretation with remediation recommendations.

Yes, you can use Python with pandas and scipy for power analysis and Bayesian statistics alternatives to plan study sampling and generate diagnostic interpretations when standard hypothesis testing assumptions are violated.

You should switch to non-parametric statistical tests instead of standard regression or correlation when systematic assumption checks fail for normality, variance homogeneity, or regression linearity, triggering remediation recommendations.