Difference between revisions of "Fisher's Exact Test"
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| Line 22: | Line 22: | ||
|- | |- | ||
! 1 | ! 1 | ||
| − | | | + | | 5 || 3 |
|- | |- | ||
! 2 | ! 2 | ||
| − | | 8 || | + | | 8 || 9 |
| + | |} | ||
| + | =FISHERSEXACTTEST([A1:B2],true) | ||
| + | |||
| + | {| class="wikitable" | ||
| + | |+FISHER'S EXACT TEST | ||
| + | |- | ||
| + | ! !!DATA-0!!DATA-1 !! SUM | ||
| + | |- | ||
| + | |- !! 5 !! 3 !! 8 | ||
| + | |- | ||
| + | |- !! 8 !! 9 !! 17 | ||
| + | |- | ||
| + | |- !! 13 !! 12 !! 25 | ||
|} | |} | ||
Revision as of 09:12, 27 February 2018
FISHERSEXACTTEST(DataRange,NewTableFlag)
- is the array of x and y values.
- is either TRUE or FALSE. TRUE for getting results in a new cube. FALSE will display results in the same cube.
is the array of x and y values.
is either TRUE or FALSE. TRUE for getting results in a new cube. FALSE will display results in the same cube.Description
- 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.
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
| A | B | |
|---|---|---|
| 1 | 5 | 3 |
| 2 | 8 | 9 |
=FISHERSEXACTTEST([A1:B2],true)
| DATA-0 | DATA-1 | SUM |
|---|