Im trying to perform a simple hypothesis test but now I need t-values for alpha = 0.01 instead of 0.05 (the default). Is there a way to do this in R?

This is what I am trying to get for alpha = 0.01: enter image description here

CodePudding user response：

If you used the t.test function in R, you can use the argument conf.level = 0.99, since the confidence level is equivalent to 1 – the alpha level. You can also read this page on Rdocumentation on the t.test function for more information on what arguments can be used

CodePudding user response：

This seems to be a statistical question rather than a programming question, and as such probably belongs on CrossValidated ...

Results table:

            Estimate Std. Error   t value     Pr(>|t|)
(Intercept) 93.10386  44.482243  2.093057 3.647203e-02
       educ 39.82828   3.314467 12.016497 3.783286e-32

When you change alpha (the cutoff value for significance testing), nothing in the table above — neither the t-statistic (t value) nor the p-value (Pr(>|t|)) — changes. The only thing that changes is the judgment of whether you rejected or failed to reject the null hypothesis. In this case, since the p-value for the intercept (0.036) is between 0.01 and 0.05, the conclusion would change from "reject H0" (alpha=0.05) to "fail to reject H0" (alpha=0.01). The p-value for educ is way less than 0.01, so the conclusion would be "reject" either way.

In most cases, base-R functions don't specify an alpha value; they let you make the decision yourself. If you do have a vector of p-values, you could implement an alpha threshold by saying

result <- ifelse(pval<alpha, "reject H0", "fail to reject H0")