Chi Square Test Calculator

Perform chi-square tests efficiently with our Chi-Square Test Calculator.

Observed Numbers (y):
(Comma separated or in separated lines)
Expected Numbers (x):
(Comma separated or in separated lines)
Chi Square (X2):

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The Chi-Square Test is a statistical test that is used to determine if there is a significant association between two categorical variables. It's a powerful tool for analyzing data and uncovering relationships between variables that can help in making informed decisions and drawing conclusions.

How to Use the Chi-Square Test Calculator

Step 1: Enter Data

First, you need to enter the observed frequencies (actual counts) for each category or group. The data is typically organized in a contingency table, where rows represent one variable, and columns represent the other variable. Make sure your table looks like this:

 Observed Numbers (y)Expected Numbers (x)...
Group 1Observed count 1Observed count 2...
Group 2Observed count 3Observed count 4...

Step 2: Set Significance Level (Alpha)

Choose the significance level (alpha) that defines the probability threshold for your test. Common values are 0.05 (5%), 0.01 (1%), or any other level of significance you prefer. The significance level represents the probability of making a Type I error (rejecting a true null hypothesis).

Step 3: Calculate

Click the "Calculate" button, and the Chi-Square Test Calculator will process your data and provide you with the following results:

Chi-Square Statistic (χ²): This is the result of the test, and it quantifies the difference between the observed and expected frequencies. A higher chi-square statistic suggests a stronger association between the variables.

Based on the p-value and the significance level you set, the calculator will provide you with a conclusion. If p ≤ α (where α is your significance level), you can reject the null hypothesis and conclude that there is a significant association between the variables.

Using the Chi-Square Test Calculator simplifies the process of analyzing categorical data, making it easier to assess whether there is a significant relationship between the variables. It's a valuable tool for researchers, statisticians, and anyone working with categorical data to draw meaningful conclusions from their analyses.