If the calculated is less the tabulated at the given alpha value you accept the null hypothesis otherwise you reject. So, you have the liberty of either using the F value or the p value. But if you are doing you calculation manually using F value is more simpler, if you are using a software then p-value is simpler.

Oct 21, 2020 · The F-test of one continuous (or just not non-binary categorical) variable is equivalent to the t-test. The F-stat is the square of the t-stat, and both tests give the same p-value (assuming a two-sided t-test). Let's simulate this in R. set.seed(2020) x <- rnorm(100) y <- x + rnorm(100) L <- lm(y~x) summary(L) The result...

So in short, Reject the null when your p value is smaller than your alpha level. You should also reject the null if your critical f value is smaller than your F Value, you should also reject the null hypothesis.The F value should always be used along with the p value in deciding whether your results are significant enough to reject the null hypothesis.

Jul 18, 2015 · This value can then be compared to the appropriate F distribution to do an F test. This can be derived ...

Jan 17, 2017 · The F-statistic is the division of the model mean square and the residual mean square. Software like Stata, after fitting a regression model, also provide the p-value associated with the F-statistic. This allows you to test the null hypothesis that …

The F statistic is a ratio of 2 different measure of variance for the data. If the null hypothesis is true then these are both estimates of the same thing and the ratio will be around 1.

As such the expected value of the F ratio will be larger than under the null hypothesis, and will also more likely be larger than one. However, the point is that both the numerator and denominator are random variables, and so is the F ratio. The F ratio is drawn from a distribution.

I'm trying to compute ANOVA effect sizes from papers that provide an F value without other information. If I understand correctly, the effect size for a single-factor ANOVA is $$ \\eta {2} = \\frac{...

May 16, 2022 · That gives you the high F-value. You need to decide on a way to deal with the fact that statistical significance does not correspond to real-world significance. What you need to decide is whether a difference between, for example, -0.43 and -0.13 is of any importance.

If you have only one variable in the model, then t-test on a single variable and its p-value will be the same as F-test on an entire model and its p-value. The difference is when you have more than one variable. In this case it is possible to have individual variable's p-values (from t-test) not significant, yet F-test p-values significant.