How to calculate p value for Fisherʼs exact test?
Fisher’s exact test is a statistical method used to determine the association between two categorical variables. It is commonly used when sample sizes are small or the data does not meet the assumptions of other statistical tests.
To calculate the p value for Fisher’s exact test, you need to follow these steps:
1. Construct a contingency table that displays the frequencies of different categories for the two variables you are studying.
2. Calculate the factorial of the marginal totals of the contingency table and use this information to calculate the total number of possible arrangements of the data.
3. Determine the probability of obtaining a table as extreme as the one you observed, or even more extreme, by chance alone.
4. This probability is the p value for Fisher’s exact test.
What is Fisherʼs exact test?
Fisher’s exact test is a statistical test used to analyze the association between two categorical variables in a contingency table. It is commonly used when sample sizes are small, and the data do not meet the assumptions of other statistical tests.
When is Fisher’s exact test used?
Fisher’s exact test is used when analyzing the association between two categorical variables in a contingency table, especially when sample sizes are small, and the data do not meet the assumptions of other statistical tests.
What are the assumptions of Fisher’s exact test?
Fisher’s exact test does not make any specific assumptions about the data distribution. It is robust to violations of assumptions like normality or homogeneity of variances that other tests may require.
What is the null hypothesis in Fisher’s exact test?
The null hypothesis in Fisher’s exact test is that there is no association between the two categorical variables being studied.
How do you interpret the p value in Fisher’s exact test?
A low p value (typically ≤ 0.05) indicates that the observed association between the two variables is unlikely to have occurred by chance alone, leading to the rejection of the null hypothesis.
What does a high p value in Fisher’s exact test indicate?
A high p value (typically > 0.05) suggests that the observed association between the two variables is likely to have occurred by chance alone, leading to the failure to reject the null hypothesis.
Can Fisher’s exact test be used for more than two categories?
Fisher’s exact test is typically used for 2×2 contingency tables, but there are extensions that allow for larger tables with more categories. However, it is most commonly applied to 2×2 tables.
What software can be used to conduct Fisher’s exact test?
Popular statistical software packages like R, SPSS, and SAS have functions or procedures for conducting Fisher’s exact test. There are also online calculators available for performing the test.
Is Fisher’s exact test more accurate than the chi-squared test?
Fisher’s exact test is considered more accurate than the chi-squared test when sample sizes are small or when the data violate the assumptions of the chi-squared test.
What is the relationship between odds ratio and Fisher’s exact test?
Fisher’s exact test is often used to calculate the odds ratio, a measure of association between two variables in a contingency table. The odds ratio can help interpret the strength and direction of the association.
What are the advantages of using Fisher’s exact test?
Fisher’s exact test is advantageous when sample sizes are small, and the data do not meet the assumptions of other statistical tests. It provides an exact probability value for the association between categorical variables.
Can Fisher’s exact test be used for non-independent samples?
Fisher’s exact test assumes that the samples are independent, meaning that the data for one group does not affect the data for the other group. If the samples are not independent, alternative statistical tests may be more appropriate.