This packet introduces you to the concept of hypothesis testing, shows you the 4 steps involved with it, and gives you a detailed example on the whole process.
Some new terms are:
Source: Greene
This video shows you how to make your decision in a hypothesis test based on the critical values for your test, and what those critical values should be for certain significance levels.
Source: YouTube
This video gives an example of a left tailed hypothesis test.
Source: YouTube
The most common alpha levels for hypothesis testing are .01 and .05. Each of the values will have corresponding critical values which you can compare with your test statistic. Here are the following critical values from the z-table.
For alpha = .01
Left tailed test: CV = -2.326
Right tailed test: CV = 2.326
Two tailed test: CV = -2.576 and 2.576
These values are bigger because you are performing a weaker test. Since you don't know if the parameter is greater or less than your value, you have to have better evidence to reject the null hypothesis.
For alpha = .05
Left tailed test: CV = -1.645
Right tailed test: CV = 1.645
Two tailed test: CV = -1.96 and 1.96
You can compare these values to your test statistic to make a decision about your null hypothesis, Ho.
Source: Greene
