This may be a good stopping point. You have strong evidence that the populations the data are sampled from are not identical. Some statisticians suggest never using Bartlett's test. It is too sensitive to minor differences that wouldn't really affect the overall variance. So if the difference in variances is not huge, and especially if your sample sizes are equal or nearly so , you might be safe just ignoring Barlett's test.
Some suggest using Levene's median test instead. Prism doesn't do this test yet , but it isn't hard to do by Excel combined with Prism.
To do Levene's test, first create a new table where each value is defined as the absolute value of the difference between the actual value and median of its group. The idea is that by subtracting each value from its group median, you've gotten rid of difference between group averages. This is logical as we would expect players that win more tournaments to have scored more birdies and to be more consistent with the number of birdies scored.
Keep in mind that visualizing variance provides indications rather than statistical confirmation of homogeneity or heterogeneity. To confirm we can perform statistical tests which I cover next.
When testing one independent variable, such as the variance of birdies across tournament wins we use the bartlett. For all the tests that follow, the null hypothesis is that all populations variances are equal, the alternative hypothesis is that at least two of them differ. Consequently, p -values less than 0. We can illustrate by testing if the variance in birdies is different among the following groups - zero tournament wins, one win, and two wins. This differs from the results from the Bartlett test above, likely because of non-normality in our data.
Similar to the assumption of normality, when we test for violations of constant variance we should not rely on only one approach to assess our data. Rather, we should understand the variance visually and by using multiple testing procedures to come to our conclusion of whether or not homogeneity of variance holds for our data.
See Levene and Glass for further discussion. An F-test is any statistical test in which the test statistic has an F-distribution under the null hypothesis. It is most often used when comparing statistical models that have been fitted to a data set, in order to identify the model that best fits the population from which the data were sampled.
In general, if your calculated F value in a test is larger than your F statistic, you can reject the null hypothesis. However, the statistic is only one measure of significance in an F Test.
You should also consider the p value. The F ratio is a statistic. When the null hypothesis is false, it is still possible to get an F ratio less than one. The larger the population effect size is in combination with sample size , the more the F distribution will move to the right, and the less likely we will be to get a value less than one.
The p-value, or probability value, tells you how likely it is that your data could have occurred under the null hypothesis. The p-value is a proportion: if your p-value is 0. The t-value measures the size of the difference relative to the variation in your sample data. Put another way, T is simply the calculated difference represented in units of standard error. The greater the magnitude of T, the greater the evidence against the null hypothesis.
Begin typing your search term above and press enter to search. Press ESC to cancel. Skip to content Home What test to use if data is not normally distributed? Ben Davis May 31, What test to use if data is not normally distributed? Which distribution is not normal? How do you know if a distribution is not normally distributed?
Does at test require a normal distribution? Which distribution is used to compare two variances?
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