Vishnu K, a doctoral student in Finance writes in a question:

Dear Professor Andrew Wheeler

Hope you are fine. I am big follower of your blog and have used it heavily to train myself. Since you welcome open questions, I thought of asking one here and I hope you don’t mind.

I was reading the blog Dave Giles and one of his blogs https://davegiles.blogspot.com/2019/10/everythings-significant-when-you-have.html assert that one must adjust for p values when working with large samples. In a related but old post, he says the same

“So, if the sample is very large and the p-values associated with the estimated coefficients in a regression model are of the order of, say, 0.10 or even 0.05, then this really bad news. Much, much, smaller p-values are needed before we get all excited about ‘statistically significant’ results when the sample size is in the thousands, or even bigger. So, the p-values reported above are mostly pretty marginal, as far as significance is concerned” https://davegiles.blogspot.com/2011/04/drawing-inferences-from-very-large-data.html#more

In one of the posts of Andrew Gelman, he said the same

“When the sample size is small, it’s very difficult to get a rejection (that is, a p-value below 0.05), whereas when sample size is huge, just about anything will bag you a rejection. With large n, a smaller signal can be found amid the noise. In general: small n, unlikely to get small p-values.

Large n, likely to find something. Huge n, almost certain to find lots of small p-values” https://statmodeling.stat.columbia.edu/2009/06/18/the_sample_size/

As Leamer (1978) points if the level of significance should be set as a decreasing function of sample size, is there a formula through which we can check the needed level of significance for rejecting a null?

Context 1: Sample Size is 30, number of explanatory variables are 5

Context 2: Sample Size is 1000, number of explanatory variables are 5

In both contexts cant, we use p-value <.05 or should we fix a very small p-value for context 2 even though both contexts relates to same data set with difference in context 2 being a lot more data points!

Worrying about p-values here is in my opinion the wrong way to think about it. You can focus on the effect size, and even if an effect is significant, it may be substantively too small to influence how you use that information.

Finance I see, so I will try to make a relevant example. Lets say a large university randomizes students to take a financial literacy course, and then 10 years later follows up to see their overall retirement savings accumulated. Say the sample is very large, and we have results of:

```
Taken Class: N=100,000 Mean=5,000 SD=2,000
No Class: N=100,000 Mean=4,980 SD=2,000
SE of Difference ~= 9
Mean Difference = 20
T-Stat ~= 2.24
p-value ~= 0.025
```

So we can see that the treated class saves more! But it is only 20 dollars more over ten years. We have quite a precise estimate. Even though those who took the class save more, do you really think taking the class is worth it? Probably not based on these stats, it is such a trivial effect size given the sample and overall variance of savings.

And then as a follow up from Vishnu:

Thanks a lot Prof Andrew. One final question is, Can we use the Cohen’s d or any other stats for effect size estimation in these cases?

Cohen’s d = (4980 – 5000) ⁄ 2000 = 0.01.

I don’t personally really worry about Cohen’s D to be honest. I like to try to work out the cost-benefits on the scales that are meaningful (although this makes it difficult to compare across different studies). So since I am a criminologist, I will give a crime example:

```
Treated Areas: 40 crimes
Non-Treated Areas: 50 crimes
```

Ignore the standard error for this at the moment. Whether a drop in 10 crimes “is worth it” depends on the nature of the treatment and the type of crime. If the drop is simply stealing small items from the store, but the intervention was hire 10 security guards, it is likely not worth it (the 10 guards salary is likely much higher than the 10 items they prevented theft of).

But pretend now that the intervention was nudging police officers to patrol more in hot spots (so no marginal labor cost) and the crimes we examined were shootings. Preventing 10 shootings is a pretty big deal, because they have such large societal costs.

In this scenario the costs-benefits are always on the count scale (how many crimes did you prevent). Doing another scale (like Cohen’s D or incident rate ratios or whatever) just obfuscates how to calculate the costs/benefits in this scenario.