Dr Andrew J. Vickers’ famous ‘Hoop story with Jordan’ describes a good interpretation of p-value and hypothesis testing. The story goes like this:
The other day I shot baskets with Michael Jordan. He shot 7 straight free throws; I hit 3 and missed 4 and then rushed to the sideline, grabbed my laptop and calculated a p-value by Fisher’s exact test.
Andrew Vickers, What is a p-value anyway? 34 Stories to Help You Actually Understand Statistics
So, what was the p-value? Let’s summarise the results and apply the test using R codes.
| Basket | No.Basket | |
| Jordan | 7 | 0 |
| Vickers | 3 | 4 |
hoop.game <- data.frame("Basket" = c(7, 3), "No Basket" = c(0, 4), row.names = c("Jordan", "Vickers"))
fisher.test(hoop.game)Fisher's Exact Test for Count Data
data: hoop.game
p-value = 0.06993
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.8498871 Inf
sample estimates:
odds ratio
Inf Now, would you take this p-value (0.07) to suggest that there is no difference between my basketball skills and those of Michael Jordan? The answer is a firm NO; it only says the experiment hadn’t proved a difference between the two players.
Reference
What is a p-value anyway? 34 Stories to Help You Actually Understand Statistics: Andrew Vickers
Low-Fat Diets Flub a Test: NYT
