We obtain a Sig. value (p) in the output beside the F statistic when using IBM SPSS. The Sig. value is a probability value that we compare to our selected alpha level such as = .05. If the Sig. value (p) is less than the selected alpha level then we reject the null hypothesis such as Sig. value (p) = .02, which is less than = .05. If the Sig. value (p) is greater than the alpha level then we fail to reject the null hypothesis such as Sig. value (p) = .15, which is greater than = .05. Comparing the computer generated Sig. value to the is an easier alternative than the historical way of going to a table of critical values and then comparing the F statistic value to the F critical value to determine whether to reject or fail to reject the null hypothesis. Most researchers use computer programs for statistical analyses so it is less common for researchers to use critical values tables since the computer programs include the information from critical values tables. The Sig. values generated from a computer program are more exact probabilities related to testing null hypotheses. It is always important to know what the null hypothesis says so that you know what the decision means when you reject or fail to reject the null hypothesis. As a researcher, sometimes we want to reject a null hypothesis and other times we want to fail to reject a null hypothesis. But what we want does not affect the decision rules that we follow. That is why we need to know what the null hypothesis is testing. For example, we test the null of three means on a dependent variable for significant differences using an = .01, H₀: ₁ = ₂ = ₃ and a one-way ANOVA. From the computer output, the F = 3.646 and the Sig. (p) = .006. What is our decision?

Group of answer choices

A: Fail to reject the null hypothesis, p > .01, no differences were found among the means

B: Reject the null hypothesis, p < .01, there is a difference among the means