Question

In a chi-square test, what would it mean if the P-value were less than 5%?

a. That there is no real difference between observed and expected values.

b. That there is a difference between observed and expected values.

c. That any difference between observed and expected values is probably not due to random chance.

d. a and c are true

e. b and c are true

Answer 1

Lets consider students,

Most of us first encounter p-values when we conduct simple hypothesis tests, although they also are integral to many more sophisticated methods. P-values are frequently misinterpreted, which causes many problems. involved at some length, but the fact remains that the p-value will continue to be one of the most frequently used tools for deciding if a result is statistically significant.

I will give you an example;

Two-sample T for BIU.In

Damper N Mean StDev SE Mean

1 40 9.91 3.02 0.48

2 so 10.14 2.77 0.39

Difference =p (1) - p (2)

Estimate for difference: -0.235

95% CI for difference: {-1.464, 0.993)

T-Test of difference = 0 {vs #): T-Value = -0.38 P-Value = 0.704 DF = &0

In the majority of analyses, an alpha of 0.05 is used as the cutoff for significance. If the p-value is less than 0.05, we reject the null hypothesis that there's no difference between the means and conclude that a significant difference does exist. If the p-value is larger than 0.05, we cannot conclude that a significant difference exists.

That's pretty straightforward, right? Below 0.05, significant. Over 0.05, not significant.

So the answer is e.