Statistics Bootcamp

Prism is a powerful tool, but without an understanding of the fundamentals of statistics, it can be intimidating. This video gives you the statistical background you need to start performing analyses in Prism with confidence.

5 Minutes

Knowing the scale of measurement for your data is an important aspect in choosing the right statistical tool and can help prevent mistakes. In this video, you will learn about the four types of data scales and see examples of each.

4 Minutes

This tutorial introduces Prism's power and sample size analysis tools. It walks through calculating sample size based on desired power, effect size, and significance level for t tests and ANOVAs, using real data and customizable options.

9 Minutes

Watch this video to learn more about P-Hacking with tips on how you can avoid it.

4 minutes

Learn about some of the ways that repeated measures can appear in an experiment

3 minutes

Learn the benefits and drawbacks of a nonparametric analysis and when it’s important to use one.

2 minutes

Running a Chi-square or Fisher’s exact test will help you determine whether or not there is a significant difference between two proportions. However, there are a few questions you should consider answering before running each test.

2 Minutes

What is simple logistic regression and why do you need it? Watch this video to find out more!

2 Minutes

olcano plots show statistical significance versus magnitude of change (fold change) allowing you to quickly identify genes with large fold changes that are also statistically significant. Watch this video to learn how you can create your own Volcano Plot.

12 Minutes

Regression is a powerful tool, but care needs to be taken when performing the analysis, making predictions, and interpreting the results. In this video, learn when it’s appropriate to use regression and how to avoid common pitfalls.

4 Minutes

Explore PCA and PCR in Prism to interpret large datasets with multicollinearity. Learn to identify principal components, interpret loadings and eigenvalues, and run regressions to reveal variable influences—ideal for hypothesis generation.

12 Minutes