The world produces more data than ever. Are you ready for it?

In today's data-driven world, you hear about making decisions based on data all the time. Hypothesis testing plays a crucial role in that process, whether you're in academia, business, or data science. Without hypothesis tests, you risk making bad decisions.

Chances are high you'll need to understand these tests to analyze your data and evaluate the work of others.

Build the knowledge for effective hypothesis testing Know when to use each test, how to use them reliably, and how to interpret the results correctly

Understand why you need hypothesis tests and how they work.
Effectively use significance levels, p-values, confidence intervals.
Select the correct type of test to answer your question.
Learn how to test means, medians, variances, proportions, distributions, counts, correlations for continuous and categorical data, and find outliers.
One-Way ANOVA, Two-Way ANOVA, and interaction effects.
Check assumptions to obtain reliable results.
Manage the error rates for false positives and false negatives.
Understand sampling distributions, the central limit theorem, and statistical power.
Know how t-tests, F-tests, chi-squared, and post hoc tests work.
Learn about differences between parametric, nonparametric, and bootstrapping methods.
Examples of many hypothesis tests.
Access free downloadable datasets so you can try it yourself.

Hypothesis Testing - pocket, Engelsk, 2020