![]() Study the individual p values to find out which of the individual variables are statistically significant.If the p value is less than the alpha level, go to Step 2 (otherwise your results are not significant and you cannot reject the null hypothesis).The statistic is just comparing the joint effect of all the variables together.įor example, if you are using the F Statistic in regression analysis (perhaps for a change in R Squared, the Coefficient of Determination), you would use the p value to get the “big picture.” Why? If you have a significant result, it doesn’t mean that all your variables are significant. The F statistic must be used in combination with the p value when you are deciding if your overall results are significant. The p value is determined by the F statistic and is the probability your results could have happened by chance. However, the statistic is only one measure of significance in an F Test. ![]() In general, if your calculated F value in a test is larger than your F critical value, you can reject the null hypothesis. The F critical value is a specific value you compare your f-value to.The value you calculate from your data is called the F Statistic or F value (without the “critical” part).In your F test results, you’ll have both an F value and an F critical value. You can use the F statistic when deciding to support or reject the null hypothesis. If you don’t have statistically significant results, you throw your test data out (as it doesn’t show anything!) in other words, you can’t reject the null hypothesis. Simply put, if you have significant result, it means that your results likely did not happen by chance. ![]() It’s similar to a T statistic from a T-Test A T-test will tell you if a single variable is statistically significant and an F test will tell you if a group of variables are jointly significant. An F statistic is a value you get when you run an ANOVA test or a regression analysis to find out if the means between two populations are significantly different. ![]()
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