Graphpad Verified Link | Chi Square

Prism often warns you if your sample size is too small for a standard Chi-square test, prompting you to use Fisher's exact test instead.

Even experienced users can make mistakes. Here are the most frequent errors when using the chi‑square test in GraphPad Prism, along with recommendations for avoiding them.

Suppose we want to investigate the relationship between smoking status and lung cancer diagnosis. We collect data on 100 patients, with 50 smokers and 50 non-smokers, and diagnose 30 lung cancer cases among smokers and 10 cases among non-smokers.

To ensure your chi-square test is valid, adhere to these guidelines: A. Sample Size Requirements chi square graphpad verified

: Entering normalized values or percentages will make your results "completely meaningless". : In Prism, select a Contingency

Closing practical tip

If your manual math matches Prism's expected values table, you can rest assured your software configuration is correct. Verify Independence of Observations Prism often warns you if your sample size

Statistically significant. You reject the null hypothesis; there is a significant association between your variables. ≥is greater than or equal to

GraphPad Prism displays the results in a new , typically organized as follows:

A less common but still valuable application is the , where you compare the observed distribution of data across categories with a theoretical distribution you expect based on prior knowledge or a biological hypothesis. For example: Suppose we want to investigate the relationship between

The analysis dialog contains two important tabs: and “Parameters.” The settings you choose here directly affect the validity of your results, so pay close attention:

The Chi-Square test is commonly used in various fields, such as:

Click the button on the toolbar, or select Analyze -> Analyze Data... from the top menu.

) that would occur if there were no association between the variables (the null hypothesis).