Top Menu

Explaining What P Values Are In Plain English

Since the P-value of 0.075 is greater than the alpha of 0.05 … wait, stop, what am I saying? Nobody I speak to on a day to day basis knows what the hell I’m talking about. If you do, can you explain what a P value is? Let me attempt to explain what it is in plain language.

When a software package runs a statistical test, it’s going to generate a ‘test statistic’ which is supposed to tell us something. There it is, I can see the ’t’ statistic is 0.9826 or the ‘chi’ statistic is 6.543.

One would have to have the brain of Einstein to be able to make sense of these test numbers and draw some understandable and accurate conclusion.

Thankfully every single one of these ‘test statistics’ comes with a P value, and the interpretation of the P value is always the same.

Now before I go on and you improvement professionals or statisticians out there jump down my throat and tell me I am not technically correct; this was not written for you. It was written specifically for those people new to business improvement who opened a book or statistical software package and wondered what that ‘P value’ thingy is.

P values sit on a continuum somewhere between 0.00 and 1.00 and they are nothing more than a measure of the ‘significance’ of something.

Every time we run a test we do so because we hypothesise about some idea – e.g. I think gender has an effect on how tall a person is.

We would then run a test to evaluate this idea and see whether we can (a) reject the idea or (b) validate that it is correct.

In this height example, we would collect a sample of data knowing that a sample might be slightly different from the overall population. We would then statistically compare the sample of heights of women against the sample of heights of men to answer our question.

Basically, the P value that would be generated tells us whether or not any difference we see in the heights is ‘significant’ or not. A little bit of difference between ‘samples’ would result in the generation of a test P value at one end of the continuum (i.e. more towards 1.00) and tell us the difference we are seeing based on a sample of data is ‘not significant’ enough to confirm our suspicions.

A lot of difference between ‘samples’ would result in the generation of a P value at the other end of the continuum (i.e. more towards 0.00) and tell us that the difference we are seeing is ‘significant’ and we can confirm our suspicions.

We would then base our ‘statistical conclusion’ on what the P value is indicating to us.

How’d I do?

No comments yet.

Leave a Reply