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P-value is the % of error you make when rejecting the null hypothesis (H0)
Si W est un test statistique, la p-valeur ou le niveau de signification atteint est le niveau de signification la plus petite probabilité alpha; pour lequel les données observées indiquent que l'hypothèse nulle doit être rejetée.
The p-value is the probability of rejecting a true null hypothesis (usually called a type 1 error by statisticians)
the probability of finding the observed, or more extreme, results when the null hypothesis (H 0) of a study question is true – the definition of 'extreme' depends on how the hypothesis is being tested.
the p-value is the probability of obtaining a result as extreme as, or more extreme than, the result actually obtained when the null hypothesis is true. If that makes your head spin like Dorothy’s house in a Kansas tornado, just pretend Glenda has waved her magic wand and zapped it from your memory. Then ponder this for a moment.
The p-value is basically the probability of obtaining your sample data IF the null hypothesis (e.g., the average cost of Cairn terriers = $400) were true. So if you obtain a p-value of 0.85, then you have little reason to doubt the null hypothesis. However, if your p-value is say 0.02, there’s only a very small chance you would have obtained that data if the null hypothesis was in fact true.
And since the p-value is a probability just like alpha, p-values also range from 0 to 1.
P value is the probability between numbers 0 to 1
