Null hypothesis rejection area

In general, an F-statistic is a ratio of two quantities that are expected to be roughly equal under the null hypothesis, which produces an F-statistic of approximately 1. In order to reject the null hypothesis that the group means are equal, we need a high F-value. It determines the significance of the groups of variables. The F critical value is also known as the F —statistic. The p-value is calculated using the sampling distribution of the test statistic under the null hypothesis, the sample data, and the type of test being done lower-tailed test, upper-tailed test, or two-sided test.

If your test statistic is positive, first find the probability that Z is greater than your test statistic look up your test statistic on the Z-table, find its corresponding probability, and subtract it from one. Then double this result to get the p-value. Calculate your T-Value by taking the difference between the mean and population mean and dividing it over the standard deviation divided by the degrees of freedom square root.

We can take toss the coin times and measure the number of times we get heads. We can stipulate that if we get heads more than 70 times then we conclude that the coin is biased.

In this case, the number of heads being more than 70 is our rejection region. Save my name, email, and website in this browser for the next time I comment. Sign in. Forgot your password? In statistics, the p-value is the probability of obtaining results at least as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct. A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis.

In this way, T and P are inextricably linked. Consider them simply different ways to quantify the "extremeness" of your results under the null hypothesis. The larger the absolute value of the t-value, the smaller the p-value, and the greater the evidence against the null hypothesis. If your test statistic is positive, first find the probability that Z is greater than your test statistic look up your test statistic on the Z-table, find its corresponding probability, and subtract it from one.

Then double this result to get the p-value. Every t-value has a p-value to go with it. A p-value is the probability that the results from your sample data occurred by chance. F-tests are named after its test statistic, F, which was named in honor of Sir Ronald Fisher. The F-statistic is simply a ratio of two variances. Variances are a measure of dispersion, or how far the data are scattered from the mean.

Larger values represent greater dispersion. In other words, an alpha level of??? But a stricter alpha level of??? Otherwise, we fail to reject the null. The p-value and rejecting the null for one- and two-tail tests. What is the p-value? I'm krista. Calculating the??? For a one-tailed, upper-tail test For a one-tailed test, first calculate your???

For a two-tailed test For a two-tailed test, first calculate your??? In that case,???