Welcome to the Hypothesis Testing page! Here, we delve into the essential tools statisticians use to draw conclusions from data. Hypothesis testing allows us to make informed decisions by evaluating evidence and determining the likelihood of certain claims. Whether you’re learning the basics or exploring more advanced techniques, this page connects you to resources that break down key concepts, explain testing types, and guide you through interpreting results with confidence. Explore the posts below to deepen your understanding of hypothesis testing and its applications in the real world.
Testing Averages Made Simple: The One-Sample t-Test Explained
If you’ve ever wanted to determine whether the average of a single group differs significantly from a known or hypothesised value, then the one-sample t-test is your statistical tool of choice. In this blog post, we’ll demystify the one-sample t-test, explain when and how to use it, and walk through a real-world example to solidify…
Independent T-Test: The Statistics Behind Comparing Groups
When you have two groups and a burning question—Are these groups significantly different?—the independent t-test provides the answer. Whether you’re testing the effectiveness of two treatments, comparing sales from two marketing strategies, or analysing test scores from two teaching methods, the independent t-test is the statistical tool to use.
Understanding the Paired T-Test: Comparing Apples to Apples
When we analyse data, we often encounter scenarios where we want to compare two sets of measurements to see if they differ significantly. But what if these two sets are inherently linked, like measurements taken from the same individuals under different conditions? Enter the paired t-test: a statistical method designed specifically for comparing “apples to…
What’s a P-Value, and Why Should You Care?
In statistics, we use p-values to help us answer questions, especially when we’re trying to understand if something happened by chance, or if there’s something more going on. Here’s a beginner-friendly dive into p-values, significance testing, and why they’re valuable in understanding data.
What is Hypothesis Testing?
Hypothesis testing is a fundamental concept in statistics, used to determine whether there is enough evidence to support a specific claim or idea about a population. Whether you’re analysing a new drug’s effectiveness, comparing business strategies, or evaluating scientific theories, hypothesis testing provides a structured approach to testing claims with a high level of confidence.
Simplifying Statistics 
