{"id":169310,"date":"2024-01-11T20:44:09","date_gmt":"2024-01-11T20:44:09","guid":{"rendered":"https:\/\/namso-gen.co\/blog\/how-to-find-a-p-value-fisher-test\/"},"modified":"2024-01-11T20:44:09","modified_gmt":"2024-01-11T20:44:09","slug":"how-to-find-a-p-value-fisher-test","status":"publish","type":"post","link":"https:\/\/namso-gen.co\/blog\/how-to-find-a-p-value-fisher-test\/","title":{"rendered":"How to find a p-value fisher test?"},"content":{"rendered":"

The Fisher’s exact test is a statistical test used to determine if there are nonrandom associations between two categorical variables. It is commonly used when the sample size is small or when the assumptions of the Chi-square test are violated. The p-value of a Fisher’s exact test indicates the strength of evidence against the null hypothesis.<\/p>\n

To find the p-value of a Fisher’s exact test, you can utilize various statistical software such as R, Python, or online calculators. These tools will take your data as input and output the p-value for you. Another way to find the p-value is by using tables that contain precomputed values corresponding to different combinations of sample sizes and observed frequencies.<\/p>\n

There are four steps involved in calculating the p-value of a Fisher’s exact test:<\/p>\n

1. State the null and alternative hypothesis: The null hypothesis assumes that there is no association between the two categorical variables, while the alternative hypothesis states that there is a significant association.<\/p>\n

2. Set the significance level: Commonly used significance levels are 0.05 or 0.01. The significance level represents the threshold below which you will reject the null hypothesis.<\/p>\n

3. Calculate the p-value: Use statistical software or tables to compute the p-value for your data.<\/p>\n

4. Conclusion: Compare the p-value to the significance level. If the p-value is less than the significance level, you reject the null hypothesis and conclude that there is a significant association between the two variables. If the p-value is greater than the significance level, you fail to reject the null hypothesis.<\/p>\n

It is essential to interpret the p-value correctly. A low p-value indicates that the observed association between the variables is unlikely to have occurred by chance. Conversely, a high p-value suggests that the observed association is likely due to random variation and does not provide enough evidence to reject the null hypothesis.<\/p>\n

In summary, finding the p-value of a Fisher’s exact test involves specifying the hypotheses, setting a significance level, calculating the p-value, and drawing a conclusion based on the comparison between the p-value and the significance level.<\/p>\n

\n
\n

Table of Contents<\/p>\nToggle<\/span><\/path><\/svg><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n