statistical-analysis

Choosing the right statistical test requires checking assumptions like normality and variance. This Skill evaluates your dataset to select appropriate tests like t-tests, ANOVA, or Bayesian alternatives, then validates assumptions before interpretation.

To report statistical results in APA format, you need effect sizes, test statistics, and p-values. This Skill processes your experimental data and generates APA-style reporting details, ensuring your academic writeups meet publication standards.

Statistical assumption checking involves verifying normality, homogeneity of variance, linearity, and outliers before hypothesis testing. This Skill runs these diagnostics automatically using scipy and statsmodels to prevent interpretation errors.

Yes, Bayesian statistics can serve as an alternative to standard hypothesis tests. This Skill uses pymc and arviz to run Bayesian alternatives when traditional tests are invalid or when you need robust probability estimates.

Calculating effect sizes and performing power analysis requires test statistics and variance estimates. This Skill computes these metrics using pandas and scipy, providing quantitative measures of magnitude alongside your hypothesis test results.

Yes, statistical analysis works seamlessly with grouped experimental data in pandas. This Skill evaluates grouped datasets to decide between standard tests, Welch corrections, or nonparametric alternatives using numpy and scipy.