# A Significantly Improved Significance Test. Not!

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It is my great pleasure to share with you a breakthrough in statistical computing. There are many statistical tests: the t-test, the chi-squared test, the ANOVA, etc. I here present a new test, a test that answers the question researchers are most anxious to figure out, a test of significance, the *significance test*. While a test like the two sample t-test tests the null hypothesis that the means of two populations are equal the significance test does not tiptoe around the canoe. It jumps right in, paddle in hand, and directly tests whether a result is significant or not.

The significance test has been implemented in R as `signif.test`

and is ready to be `source`

d and run. While other statistical procedures bombards you with useless information such as parameter estimates and confidence intervals `signif.test`

only reports what truly matters, the one value, the p-vale.

For your convenience `signif.test`

can be called *exactly* like `t.test`

and will return the same p-value in order to facilitate p-value comparison with already published studies. Let me show you how `signif.test`

works through a couple of examples using a dataset from the RANDOM.ORG database:

# Sourcing the signif.test function source("http://www.sumsar.net/files/posts/2014-02-12-a-significantly-improved-test/significance_test.R") # A one sample signif.test signif.test(c(7.6, 5.9, 5.2, 4.2, -1))