Fisher’s Exact Test vs Chi-Square

Differences between Fisher’s Exact Test and Chi-Square are added in the table below:

Fisher’s Exact Test

Chi-Square Test

Fisher’s Exact Test is a non-parametric test, which means it does not make any assumptions about the underlying distribution of the data.

Chi-Square Test is a parametric test that assumes the data follows a certain probability distribution (typically the chi-square distribution).

It is particularly useful when dealing with small sample sizes or when the expected frequencies in some cells of the contingency table are less than 5.

It is based on the difference between the observed frequencies and the expected frequencies under the null hypothesis of independence.

Fisher’s Exact Test is preferred when dealing with small sample sizes or when the expected frequencies in some cells of the contingency table are low.

Chi-Square Test is preferred when dealing with small sample sizes or when the expected frequencies in some cells of the contingency table are high.

It is considered more accurate than the Chi-Square Test for small sample sizes, as it does not rely on approximations.

Chi-Square Test is more widely used due to its simplicity and applicability to larger sample sizes.

Fisher Exact Test

Fisher’s exact test is widely used in medical research and other fields where sample sizes are small and rare events are common. Compared to other methods such as the chi-square test, it allows for a more accurate assessment of the relationship between variables in such situations.

Fisher’s exact test allows you to calculate the probability of obtaining a frequency distribution in a contingency table that is more extreme than the observed data, assuming the null hypothesis of independence. Sizes are particularly useful when there are rare events or small cell numbers.

Table of Content

  • What is Fisher’s Exact Test?
    • When Should We Use Fisher’s Exact Test?
  • How to Interpret Fisher Exact Test?
  • Purpose and Scope of Fisher’s Exact Test
  • Fisher’s Exact Test vs Chi-Square
  • Examples on Fisher’s Exact Test
  • FAQs on Fisher Exact Test

Similar Reads

What is Fisher’s Exact Test?

Fisher’s exact test says the null hypothesis of independence applies hypergeometric distribution of the numbers in the cells of the table. Many packages provide the results of Fisher’s exact test for 2 × 2 contingency tables but not for bigger contingency tables with more rows or columns....

How to Interpret Fisher Exact Test?

If the p-value is significant and the odds ratio and confidence interval are greater than 1.0, the treatment group is more likely to achieve the outcome. If the p-value is significant and the odds ratio and confidence interval are less than 1.0, the treatment group is less likely to achieve the outcome. If the columns represent study groups and the rows represent outcomes, the null hypothesis is that the probability of a particular outcome is unaffected by the study group, and the test assesses whether the two study groups differ in their proportions. can be interpreted to mean. result....

Purpose and Scope of Fisher’s Exact Test

This test can be applied to categorical data obtained by classifying objects using two different methods. The aim is to assess the significance of the relationship (contingency) between these two classifications. For example, in Fisher’s original illustration, the order in which the milk or tea was poured into the cup could be the criterion for classification....

Fisher’s Exact Test vs Chi-Square

Differences between Fisher’s Exact Test and Chi-Square are added in the table below:...

Examples on Fisher’s Exact Test

Example 1:...

FAQs on Fisher Exact Test

Where is Fisher Exact Test used for?...