**Mad (Data) Scientist**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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Sadly, the concept of p-values and significance testing forms the very core of statistics. A number of us have been pointing out for decades that p-values are at best underinformative and often misleading. Almost all statisticians agree on this, yet they all continue to use it and, worse, teach it. I recall a few years ago, when Frank Harrell and I suggested that R place less emphasis on p-values in its output, there was solid pushback. One can’t blame the pusherbackers, though, as the use of p-values is so completely entrenched that R would not be serving its users well with such a radical move.

And yet, wonder of wonders, the American Statistical Association has finally taken a position against p-values. I never thought this would happen in my lifetime, or in anyone else’s, for that matter, but I say, Hooray for the ASA!

To illustrate the problem, consider the one of the MovieLens data sets, consisting of user ratings of movies. There are 949 users. Here is an analysis in which I regress average rating per user against user age and gender:

```
> head(uu)
userid age gender occup zip avg_rat
1 1 24 0 technician 85711 3.610294
2 2 53 0 other 94043 3.709677
3 3 23 0 writer 32067 2.796296
4 4 24 0 technician 43537 4.333333
5 5 33 0 other 15213 2.874286
6 6 42 0 executive 98101 3.635071
> q summary(q)
...
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.4725821 0.0482655 71.947 < 2e-16 ***
age 0.0033891 0.0011860 2.858 0.00436 **
gender 0.0002862 0.0318670 0.009 0.99284
...
Multiple R-squared: 0.008615, Adjusted R-squared: 0.006505
```

Woohoo! Double-star significance on age! P-value of only 0.004! Age is a highly-significant predictor of movie ratings! Older people give higher ratings!

Well, no. A 10-year age difference corresponds to only a 0.03 difference in ratings — quite minuscule in light of the fact that ratings take values between 1 and 5.

The problem is that with large samples, significance tests pounce on tiny, unimportant departures from the null hypothesis, in this case H_{0}: β_{age} = 0, and ironically declare this unimportant result “significant.” We have the opposite problem with small samples: The power of the test is low, and we will announce that there is “no significant effect” when in fact we may have too little data to know whether the effect is important.

In addition, there is the hypocrisy aspect. Almost no null hypotheses are true in the real world, so performing a significance test on them is absurd and bizarre.

Speaking of hypocrisy: As noted above, instructors of statistics courses all know of the above problems, and yet teach testing anyway, with little or (likely) no warning about this dangerous method. Those instructors also do testing in their own work.

My hat is off to ASA for finally taking some action.

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**Mad (Data) Scientist**.

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