4 Statistics you (probably) learned: T-tests, ANOVAs, and correlations

 

This chapter covers

  • Breaking down summary statistics and their underlying logic
  • Using parametric statistical tests appropriately
  • Understanding and managing the limitations of parametric statistical tests

Statistical tests can create rigor and alignment in the interpretation of numerical differences. There are common sets of methods used by most statisticians, social scientists, and analysts. Across a wide variety of domains of study and types of questions, practitioners use similar criteria to evaluate the coefficients of statistical tests to conclude whether or not they achieve statistical significance.

Despite these benefits, there are assumptions and limitations associated with common statistical tests, and there is a troublesome history associated with their development and widespread use. We will cover the context and development of common statistical tests, coefficients, and evaluation criteria, and we’ll break down the mathematical logic behind each approach. These skills will enable you to share highly accurate and actionable results with your stakeholders.

4.1 The logic of summary statistics

4.1.1 Summarizing properties of your data

4.1.2 Recap

4.1.3 Exercises

4.2 Making inferences: Group comparisons

4.2.1 Parametric tests

4.2.2 Exercises

4.3 Making inferences: Correlation and regression

4.3.1 Correlation coefficients

4.3.2 Regression modeling

4.3.3 Reporting on correlations and regressions

4.3.4 Exercises

Summary