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Do you reject the null if p Alpha?
Alpha sets the standard for how extreme the data must be before we can reject the null hypothesis. The p-value indicates how extreme the data are. If the p-value is less than or equal to the alpha (p< . 05), then we reject the null hypothesis, and we say the result is statistically significant.
At what alpha level is a null hypothesis typically rejected?
In null hypothesis testing, this criterion is called α (alpha) and is almost always set to . 05. If there is less than a 5% chance of a result as extreme as the sample result if the null hypothesis were true, then the null hypothesis is rejected.
Why do you reject null hypothesis when p value < alpha?
Another line of thought – if the p-value is a way of saying how extreme a test statistic is for our sample data and seems to have a low probability, then how does it constitute evidence against the null hypothesis? Where am I going wrong?
Which is the smallest level of significance for rejecting the null hypothesis?
The p-value (or the observed level of significance) is the smallest level of significance at which you can reject the null hypothesis, assuming the null hypothesis is true. You can also think about the p-value as the total area of the region of rejection. Remember that in a one-tailed test, the regi
When to use alpha as a criterion for rejecting H0?
A common rule is that when the probability is less than 5% (i.e., p < .05) that a sample mean drawn from the null population would be as large as the obtained sample mean, we conclude that the sample did not come from the null population. This probability criterion (e.g., .05) is called alpha (α).
Is the p-value less than or equal to Alpha?
There are two possibilities that emerge: The p-value is less than or equal to alpha. In this case, we reject the null hypothesis. When this happens, we say that the result is statistically significant. In other words, we are reasonably sure that there is something besides chance alone that gave us an observed sample.