What does a Fisher test p-value indicate?

**What does a Fisher test p-value indicate?**

The Fisher test, also known as Fisher’s exact test, is a statistical test used to determine the significance of association between two categorical variables in a contingency table. It calculates a p-value, which indicates the probability of obtaining the observed data (or more extreme) if there is no association between the variables. In simpler terms, the p-value tells us the strength of evidence against the null hypothesis of no association.

What is a p-value?

A p-value is a measure of the strength of evidence against the null hypothesis.

How is the Fisher test conducted?

The Fisher test involves calculating the probability of observing a distribution as extreme or more extreme than the observed distribution, assuming that the null hypothesis is true.

What is a contingency table?

A contingency table is a way to summarize two categorical variables in a tabular format. It shows the frequencies or proportions of the variables.

When should the Fisher test be used?

The Fisher test is appropriate when analyzing categorical data and the sample size is small, or when any of the expected cell frequencies are less than 5.

What does it mean if the p-value is small?

A small p-value (typically less than 0.05) indicates strong evidence against the null hypothesis and suggests that there may be an association between the variables.

What does it mean if the p-value is large?

A large p-value (typically greater than 0.05) suggests weak evidence against the null hypothesis, implying that there may not be a significant association between the variables.

Can a p-value be exactly 0?

No, a p-value cannot be exactly 0. It is always a small positive value.

Does a small p-value indicate the presence of a strong association?

No, a small p-value indicates strong evidence against the null hypothesis, but it does not provide information about the magnitude or strength of the association.

Can the Fisher test determine causality?

No, the Fisher test is a statistical tool that can identify association, but it cannot establish causality.

What other tests can be used to analyze categorical data?

Other commonly used tests for categorical data include the chi-square test and the G-test.

Is a Fisher test appropriate for continuous data?

No, the Fisher test is specifically designed for categorical data. For continuous data, other tests such as t-tests or ANOVA are more appropriate.

Can the Fisher test be used for more than two categorical variables?

Yes, the Fisher test can be extended to contingency tables with more than two categorical variables, but the calculation becomes more complex.

In conclusion, the Fisher test p-value indicates the probability of observing the observed data or more extreme if the null hypothesis of no association is true. It helps researchers assess the strength of evidence against the null hypothesis and determine the presence of an association between categorical variables. However, it is important to remember that the Fisher test alone cannot establish causality and should be interpreted alongside other relevant information and statistical techniques.

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