Today our goal is to cover hypothesis testing and the basic z-test, as these are fundamental to understanding how the t-test works. We’ll return to the t-test soon — with real data.

This article explores the concept of statistical hypothesis testing, its process, and how organizations can leverage it to make better business decisions. We’ll also examine real-world examples ...

We illustrate our testing procedures using two real data examples and provide recommendations for plant-disease researchers in the field. Published quarterly since 1996, the Journal of Agricultural, ...

A hypothesis test is where we examine the data and decide which of the two alternative hypotheses is more believable given the evidence we have. We begin by assuming the null hypothesis is true.

Sample surveys often have complex sample designs with multistage cluster sampling, stratification, and differential selection probabilities. This article is concerned with testing the null hypothesis ...

A type II error is a statistical term referring to accepting a false null hypothesis. It contrasts with a type I error that occurs when rejecting a true null hypothesis.

The Brookbush Institute continues to enhance education with new courses, a modern glossary, an AI Tutor, and a client pr ...