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If you’ve ever stared at a 2x2 contingency table, wondering if your treatment group truly outperformed the control, you’ve likely met the Chi-Square test. It’s the gold standard for analyzing categorical data.
The Chi-Square test is powerful but fragile. Incorrect data entry, ignored assumptions, or misapplied corrections can lead to retractions or false discoveries. By following the workflow in GraphPad Prism—checking expected counts, comparing with Fisher’s exact test, and verifying degrees of freedom—you ensure that your conclusions are robust.
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If you’ve ever stared at a 2x2 contingency table, wondering if your treatment group truly outperformed the control, you’ve likely met the Chi-Square test. It’s the gold standard for analyzing categorical data.
The Chi-Square test is powerful but fragile. Incorrect data entry, ignored assumptions, or misapplied corrections can lead to retractions or false discoveries. By following the workflow in GraphPad Prism—checking expected counts, comparing with Fisher’s exact test, and verifying degrees of freedom—you ensure that your conclusions are robust.
