What does the p-value tell you in statistical significance testing?

The p-value, or probability value, tells you how likely it is that your data could have occurred under the null hypothesis. The p-value tells you how often you would expect to see a test statistic as extreme or more extreme than the one calculated by your statistical test if the null hypothesis of that test was true.

What do you mean by type 1 error and Type 2 error?

In statistics, a Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s actually false.

What is Type 2 error in statistics?

A type II error is a statistical term used within the context of hypothesis testing that describes the error that occurs when one accepts a null hypothesis that is actually false. This is a type II error because we accept the conclusion of the test as negative, even though it is incorrect.

What is statistical error?

A statistical error is the (unknown) difference between the retained value and the true value. Context: It is immediately associated with accuracy since accuracy is used to mean “the inverse of the total error, including bias and variance” (Kish, Survey Sampling, 1965). The larger the error, the lower the accuracy.

How do you find the p-value of a test statistic?

If Ha contains a greater-than alternative, find the probability that Z is greater than your test statistic (look up your test statistic on the Z-table, find its corresponding probability, and subtract it from one). The result is your p-value.

What does p 05 mean what does p .002 mean?

P > 0.05 is the probability that the null hypothesis is true. 1 minus the P value is the probability that the alternative hypothesis is true. A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected. A P value greater than 0.05 means that no effect was observed.

Is p-value of 0.004 significant?

In other words, the lower the p-value, the less compatible the data is to the null hypothesis (i.e. despite both being significant, p = 0.04 is a weaker significance value than p = 0.004 and therefore we would be more confident that the results are ‘true’ with p = 0.004), If we are confident that all assumptions were …