It is my great pleasure to share with you a breakthrough in statistical computing. There are many statistical tests: the ttest, the chisquared 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 ttest 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 pvale.
For your convenience signif.test
can be called exactly like t.test
and will return the same pvalue in order to facilitate pvalue 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/20140212asignificantlyimprovedtest/significance_test.R") # A one sample signif.test signif.test(c(7.6, 5.9, 5.2, 4.2, 1))
## Test of Significance ## ## H0: p will be more than 0.05 ## H1: p will be less than 0.05 ## ## Result ## significant (p = 0.0395)
# A two sample signif.test signif.test(c(0.7, 4.4, 7.8, 3.8), c(17.9, 22.9, 16.3, 19.1))
## Test of Significance ## ## H0: p will be more than 0.05 ## H1: p will be less than 0.05 ## ## Result ## extremely significant (p < 0.001)
Except for the pvalue
signif.test
also reports a verbal description of the effect size of the pvalue, for example:
# An unsuccessful experiment signif.test(c(12.4, 7.9, 9.7), c(13.9, 7.7, 9.9), paired = TRUE)
## Test of Significance ## ## H0: p will be more than 0.05 ## H1: p will be less than 0.05 ## ## Result ## not significant (n.s.)
# A successful experiment signif.test(c(58.6, 62.7, 68.5, 58.8, 75.4))
## Test of Significance ## ## H0: p will be more than 0.05 ## H1: p will be less than 0.05 ## ## Result ## significant beyond doubt (p < 0.0001)
An interesting situation, that oh so many researchers have been battling with, is when a result is almost significant. Here
signif.test
uses the database compiled by Matthew Hankins to every time give a new example of how such a result could be presented in writing.
signif.test(c(3.9, 8.9, 1.2, 8.9, 2.1))
## Test of Significance ## ## H0: p will be more than 0.05 ## H1: p will be less than 0.05 ## ## Result ## practically significant (p = 0.0831)
signif.test(c(3.9, 8.9, 1.2, 8.9, 2.1))
## Test of Significance ## ## H0: p will be more than 0.05 ## H1: p will be less than 0.05 ## ## Result ## on the very fringes of signiﬁcance (p = 0.0831)
signif.test(c(3.9, 8.9, 1.2, 8.9, 2.1))
## Test of Significance ## ## H0: p will be more than 0.05 ## H1: p will be less than 0.05 ## ## Result ## fell barely short of significance (p = 0.0831)
Download
signif.test
from here orsource
it directly as above to see many more useful formulations. A current limitation ofsignif.test
is that it compares at most two groups. If your data contains more groups you can compare them two at a time, surely some combination is going give a significant result!Or perhaps I’m just kidding…
pvalues Suck
Sorry for being blunt, but it is true. pvalues do not answer the question (dare I say most) people think/hope they do: “Is there a difference?” What is worse, the question most people think pvalues answer (but they don’t) is not the right question to ask 95% of the time! In very few situations the interesting question is whether there is a difference, the interesting question is almost always: How large is the difference? What does pvalues tell us about magnitudes? Zip!
pvalues are visual noise taking up precious journal space that could be filled with useful stuff such as actual estimates, effect sizes, scatter plots, confidence or credible intervals, Rcode, AICs and DICs, box plots, yes sometimes even white space would be an improvement. I’m not going to rant more about pvalues here, they have already been accurately characterized by others:
 Dance of the pvalues, a fun and quick video about some problems with pvalues.

The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives. A book that forcefully criticizes “the sizeless stare of statistical significance” and argues for less p and more oomph in science. The arguments are also available in this shorter article.

On John D. Cook’s blog: pvalues are inconsistent and Five criticisms of significance testing.

Johnson, D. H. (1999). The insignificance of statistical significance testing. The journal of wildlife management, 763772. pdf

Wagenmakers, E. J. (2007). A practical solution to the pervasive problems of p values. Psychonomic bulletin & review, 14(5), 779804. pdf

Kruschke, J. K. (2010). What to believe: Bayesian methods for data analysis. Trends in cognitive sciences, 14(7), 293300. pdf
If you just replace “disproved” with “debunked” this xkcd comic would be a pretty accurate description of how I feel about pvalues:
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