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 ...

Learn how two-tailed tests determine statistical significance in hypothesis testing by evaluating if a sample differs from a population mean. Discover real-world applications.

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.

Most statistical analyses use a sampling distribution to make inference , hence to make valid inference we also need to assume that the units of observation in our sample are independent and from a ...

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.

We propose a testing procedure for mediation effects of high dimensional continuous mediators. We characterize the marginal mediation effect, the multivariate component-wise mediation effects, and the ...

In this article, we assume that categorical data are distributed according to a multinomial distribution whose probabilities follow a loglinear model. The inference problem we consider is that of ...