Question 1

What is the Chi-Square test and when should I use it?

Accepted Answer

The Chi-Square test determines if there is a significant relationship between categorical variables. Use it to test for independence between variables or to assess the goodness-of-fit between expected and observed frequencies.

Question 2

What data format is required for the test?

Accepted Answer

You need frequency data in a contingency table format. Enter observed frequencies for each category combination, and the tool will calculate expected frequencies and test statistics.

Question 3

How do I interpret the p-value and test results?

Accepted Answer

A p-value less than 0.05 (typically) indicates a significant relationship between variables. The tool provides the chi-square statistic, degrees of freedom, and p-value for interpretation.

Question 4

What assumptions must be met for valid results?

Accepted Answer

Chi-Square tests require: independent observations, categorical data, sufficient sample size, and random sampling from a general population.

Question 5

Can I test more than two variables simultaneously?

Accepted Answer

Standard Chi-Square tests work with two variables. For multiple variables, you may need more advanced statistical methods such as log-linear analysis or corrections for multiple testing.

Question 6

What are degrees of freedom in a Chi-Square test?

Accepted Answer

Degrees of freedom (df) represent the number of independent values that can vary in an analysis. For a contingency table, they are calculated as (number of rows - 1) * (number of columns - 1).

Question 7

When should I use a Goodness-of-Fit test instead of an Independence test?

Accepted Answer

A Goodness-of-Fit test is used to check if observed frequencies of a single categorical variable match an expected distribution. An Independence test is used to determine if there is a relationship between two categorical variables.

Question 8

What does a high Chi-Square value mean?

Accepted Answer

A high Chi-Square value indicates a large discrepancy between observed and expected frequencies, suggesting that the variables are likely not independent and there is a significant relationship between them.

A/B testing Calculator: Chi-Square Test

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Expected distributions of variants A and B

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Features of the "Chi-Square Test"

Check Statistical Significance

Used in Marketing Tests and A/B Experiments

Automatic Result Calculation

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A/B testing Calculator: Chi-Square Test

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