Difference between revisions of "Fisher's Exact Test"

Revision as of 09:06, 27 February 2018

FISHERSEXACTTEST(DataRange,NewTableFlag)

$DataRange$ is the array of x and y values.
$NewTableFlag$ is either TRUE or FALSE. TRUE for getting results in a new cube. FALSE will display results in the same cube.

This function gives the test statistic of the Fisher's Exact Test.
Since this method is more computationally intense, it is best used for smaller samples.
Like the chi-square test for (2x2) tables, Fisher's exact test examines the relation between two dimensions of the table (classification into rows vs. columns).
For experiments with small numbers of participants (below 1,000), Fisher’s is more accurate than the chi-square test or G-test.
The null hypothesis is that these two classifications are not different.
The P values in this test are computed by considering all possible tables that could give the row and column totals observed.

Unlike other statistical tests, there isn’t a formula for Fisher’s.
To get a result for this test, calculate the probability of getting the observed data using the null hypothesis that the proportions are the same for both sets.

Spreadsheet
	A	B
1	24	13
2	8	20

@@ Line 10: / Line 10: @@
 * The null hypothesis is that these two classifications are not different.
 * The P values in this test are computed by considering all possible tables that could give the row and column totals observed.
+==Assumptions==
+* Unlike other statistical tests, there isn’t a formula for Fisher’s.
+* To get a result for this test, calculate the probability of getting the observed data using the null hypothesis that the proportions are the same for both sets.
+==Example==
+{| class="wikitable"
+|+Spreadsheet
+|-
+! !! A !! B
+|-
+! 1
+| 24 || 13
+|-
+! 2
+| 8 || 20
+|}