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# Hypothesis Testing

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Author: Al Greene
##### Description:

Identify and give examples to explain the four step process of Hypothesis testing
Understand and interpret one- and two-sided hypothesis tests

This packet gives you a brief introduction to the concept of hypothesis testing. You will learn the four steps to the process, how to interpret your results, and what kind of tests there are. There is also a comprehensive example video at the bottom that will walk you through the process step by step.

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Tutorial

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:

• Hypothesis
• test statistic
• p-value
• critical value

Source: Greene

## Hypothesis Testing

This powerpoint goes over the four basic steps of a hypothesis test and helps you understand how to make a decision and conclusion about your test.

Source: Greene

## Significance Level in Hypothesis Testing

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.

## Hypothesis Testing Example

This video gives an example of a left tailed hypothesis test.

## Critical Values for Hypothesis Testing

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

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