Hypothesis Testing in Python Data Camp

Overview

Context

Hypothesis testing lets you answer questions about your datasets in a statistically rigorous way. 

Working with real-world data, including Stack Overflow user feedback and supply-chain data for medical supply shipments, you'll gain a deep understanding of how these tests work and the key assumptions that underpin them. You'll also discover how non-parametric tests can be used to go beyond the limitations of traditional hypothesis tests.

What you'll learn in this Hypothesis Testing in Python course offered by Data Camp:

• Differentiate between Type I and Type II errors and their consequences for statistical conclusions
• Distinguish between parametric and non-parametric approaches based on assumptions of normality, sample size, and independence
• Evaluate p-values, confidence intervals, and standardized test statistics produced by Python libraries to determine whether to reject the null hypothesis at a specified alpha
• Identify the suitable hypothesis test in Python (z-test, t-test, ANOVA, proportion test, chi-square, or non-parametric) for a given research question, data type, and sample conditions
• Recognize the correct null and alternative hypotheses, significance level, and tail direction for typical analytical scenarios