# It’s time to talk about ditching statistical significance

#### Bev D

##### Heretical Statistician
Staff member
Super Moderator
I hadn’t read it yet, but certainly it isn’t the first. (Later today I’ll post some other article titles and link to the free ones). You can actually start with Deming’s 1975 article “On probability as a basis for action”.

I have been advocating for this for some. In my 35 years of solving hundreds of complex problems, running insightful MSAs, process capability studies etc. I’ve never used a p value; I never checked for Normality, etc.

The thing is that the whole null hypothesis / p value / normal centric view is a hodgepodge ritual that no founding statistician (Fisher, Pearson) ever advocated for. It doesn’t add insight, it obfuscates. It enables a lack of thought, understanding or insight. It is black box. Just read most of the postss here about posters who don’t understand the results of their analyses. It promotes “I’ve got some data, what statistical tests can I do on it?”

#### Miner

##### Forum Moderator
Staff member
For me, the biggest single issue is that statistical significance (as determined by the p-value) is often detached from practical significance. This has gotten worse with access to big data, where extremely large sample sizes (in some cases, the entire population) will provide a statistically significant outcome that is of no practical importance. The only way to balance statistical and practical significance is to determine practical significance in advance, determine the sample size that will balance statistical significance with this value then run your experiment with that sample size. More often than not, the experimenter just takes the data they have and run with it.

I am not ready to throw out the concept of hypothesis testing because great strides have been made using it. In addition, you can usually run a confirmation experiment in industrial experiments to sort it out. Notice the replication crisis seems limited to soft sciences where they either cannot or will not run confirmation studies and the primary goal is to publish. In industrial statistics the primary goal is to achieve demonstrated improvements, and there are checks and balances in place to prevent wild claims with no basis (e.g., financial verification).

#### John Predmore

Trusted Information Resource
The editorial I read yesterday, on the same subject, was from the American Statistician.
The ASA's Statement on p-Values: Context, Process, and Purpose
https://amstat.tandfonline.com/doi/full/10.1080/00031305.2016.1154108
In summary, "the American Statistical Association (ASA) believes that the scientific community could benefit from a formal statement clarifying several widely agreed upon principles underlying the proper use and interpretation of the p-value. "

#### Bev D

##### Heretical Statistician
Staff member
Super Moderator
Miner - I understand your position and experience. My experience is a bit different. While the null hypothesis and p value can be helpful if we remember replication, the role homogeneity and practical importance, they aren’t necessarry. And unfortunately too many ‘teachers’ dont’ Talk about replication, homogeneity and practical importance. The recent misuse with big data is just the most recent example. And in my experience the misuse is not limited to the social sciences where planned experiments are difficult. The seduction is that a single small sample with a p value doesnt’ require much ‘work’ or that really hard thing: thinking. Those of us who actually understand it and can use it appropriately are rare.

#### bobdoering

Trusted Information Resource
My first question is usually: What is the distribution? My second one is: What should it be? No statistical calculation can answer the second one - that one takes some thought.

#### optomist1

##### A Sea of Statistics
Super Moderator
".....helpful if we remember replication, the role homogeneity and practical importance, ...that really hard thing: thinking ", the quote pretty much condenses the matter, at least for me. Statistics Software is an extremely powerful and quick tool....but requires conscious thought

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