What does a Fisher test p-value signify?

What does a Fisher test p-value signify?

The Fisher test, also known as Fisher’s exact test, is a statistical test used to analyze categorical data and determine the significance of the association between two categorical variables. The p-value obtained from the Fisher test is an essential measure that helps researchers determine the strength of evidence against the null hypothesis.

**The Fisher test p-value signifies the strength of evidence against the null hypothesis and indicates the statistical significance of the association between two categorical variables.**

When conducting a Fisher test, the null hypothesis assumes that there is no association between the two categorical variables being analyzed. On the other hand, the alternative hypothesis suggests that there is a significant association between the variables.

The p-value determines the significance of this association by indicating the likelihood of observing the observed data or more extreme results under the assumption of no association (null hypothesis). A low p-value suggests strong evidence against the null hypothesis and implies that the association between the variables is statistically significant.

Here are some frequently asked questions related to Fisher test p-values:

1. Why is the Fisher test used?

The Fisher test is used to analyze the association between two categorical variables when the sample size is small or when the assumptions of other tests, such as the chi-square test, are not met.

2. How is the Fisher test different from the chi-square test?

While both tests analyze associations between categorical variables, the Fisher test is suitable for small sample sizes, while the chi-square test is applicable for large samples. The Fisher test provides exact probabilities, whereas the chi-square test relies on approximations.

3. How can I interpret the p-value obtained from the Fisher test?

If the p-value is less than a predetermined significance level (e.g., 0.05), it suggests that the association between the variables is statistically significant. Conversely, if the p-value is greater, it indicates that the observed association could likely be due to chance.

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

Yes, the Fisher test can be used to analyze the association between multiple categorical variables simultaneously, as long as the appropriate contingency table is constructed.

5. When should I use the Fisher test instead of regression analysis?

The Fisher test is suitable when only categorical variables are involved. If you have both categorical and continuous variables, regression analysis may provide a more appropriate method.

6. What if I have missing data in my contingency table?

When there are missing data points, you may need to make an informed decision on how to handle them. If the data are considered missing at random, you may exclude those cases, or utilize imputation techniques to estimate missing values.

7. Can I use the Fisher test for non-independent data?

The Fisher test assumes independence between the categorical variables being analyzed. If the variables are dependent, such as paired or matched data, alternative tests such as the McNemar test may be more suitable.

8. Is the Fisher test affected by the sample size?

Yes, the Fisher test can be used for small sample sizes, making it appropriate for studies with limited data. However, as the sample size grows larger, the chi-square test becomes more appropriate.

9. What is the difference between one-tailed and two-tailed p-values?

A one-tailed p-value tests whether an association is specifically greater than or less than expected. In contrast, a two-tailed p-value tests whether there is any significant association, regardless of its direction.

10. Can I interpret effect size using the Fisher test?

The Fisher test does not provide a direct measure of effect size. Instead, it focuses on the statistical significance of the association. However, other measures such as odds ratios or relative risk can be used to estimate effect size.

11. Is it possible to calculate a confidence interval using the Fisher test?

The Fisher test does not inherently provide confidence intervals. However, you can calculate confidence intervals for effect size measures, such as odds ratios, to estimate the range within which the true association might lie.

12. Can I use the Fisher test for nominal and ordinal variables?

The Fisher test is primarily used for two nominal variables. However, if one of the variables is ordinal, you may need to recode it into categories to perform the Fisher test. Alternatively, other tests, such as the Mann-Whitney U test, may be more appropriate for analyzing ordinal data.

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