Respuesta :
The p-value in statistics quantifies the evidence against a null hypothesis. A low p-value suggests data is inconsistent with the null, potentially favoring an alternative hypothesis. Common significance thresholds are 0.05 or 0.01.
The p value is a number, calculated from a statistical test, that describes how likely you are to have found a particular set of observations if the null hypothesis were true.
P values are used in hypothesis testing to help decide whether to reject the null hypothesis. The smaller the p value, the more likely you are to reject the null hypothesis. We use -values to make conclusions in significance testing. More specifically, we compare the -value to a significance level to make conclusions about our hypotheses.
If the -value is lower than the significance level we chose, then we reject the null hypothesis in favor of the alternative hypothesis . If the -value is greater than or equal to the significance level, then we fail to reject the null hypothesis , but this doesn't mean we accept. To summarize: Let's try a few examples where we use
-values to make conclusions.
Example 1
Alessandra designed an experiment where subjects tasted water from four different cups and attempted to identify which cup contained bottled water. Each subject was given three cups that contained regular tap water and one cup that contained bottled water (the order was randomized). She wanted to test if the subjects could do better than simply guessing when identifying the bottled water.
Her hypotheses were vs. (where is the true likelihood of these subjects identifying the bottled water).
The experiment showed that of the subjects correctly identified the bottle water. Alessandra calculated that the statistic
had an associated P-value of approximately.
