How to do hypothesis testing. Hypothesis testing and p-values (video) | khan academy
That is, since the P-value, 0.
Instead, we have a continual decrease in the probability of obtaining sample means that are further from the null hypothesis value.
That is different, right? In fact, if we took multiple random samples of the same size from the same population, we could plot a distribution of the sample means. Formulate an analysis plan.
After all, we took a random sample and our sample mean of Fortunately, I can create a plot of sample means without collecting many different random samples!
Given a test statistic and its sampling distribution, a researcher can assess probabilities associated with the test statistic.
That is, if one is true, the other must be false; and vice versa. Note that we would not reject H0: Specify the null and alternative hypotheses.
Applications of the General Hypothesis Testing Procedure
That is, the two-tailed test requires taking into account the possibility that the test statistic could fall into either tail and hence the name "two-tailed" test. A hypothesis test helps assess the likelihood of this possibility!
Using the known distribution of the test statistic, calculate the P-value: When the parameter in the null hypothesis involves categorical data, you may use a chi-square statistic as the test statistic.
Note that the P-value for a two-tailed test is always two times the P-value for either of the one-tailed tests.
If the sample findings are unlikely, given the null hypothesis, the researcher rejects the null hypothesis. The P-value is the probability of observing a sample statistic as extreme as the test statistic, assuming the null hypotheis is true.
Applications of the General Hypothesis Testing Procedure The next few lessons show how to apply the general hypothesis testing procedure to different kinds of statistical problems.
The Need for Hypothesis Tests
Using the sample data and assuming the null hypothesis is true, calculate the value of the test statistic. Sampling error is the difference between a sample and the entire population.
How do these tests really work and what does statistical significance actually mean? Our goal is to determine whether our sample mean is significantly different from the null hypothesis mean.
As you can see, there is no magic place on the distribution curve to make this determination.