# Steps in Hypothesis Testing

**Step 1: State the Null Hypothesis**

The null hypothesis can be thought of as the opposite of the “guess” the research made (in this example the biologist thinks the plant height will be different for the fertilizers). So the null would be that there will be no difference among the groups of plants. Specifically in more statistical language the null for an ANOVA is that the means are the same

**Step 2: State the Alternative Hypothesis**

*[Math Processing Error]*

The reason we state the alternative hypothesis this way is that if the Null is rejected, there are many possibilities.

For example, *[Math Processing Error]* is one possibility, as is *[Math Processing Error]*. Many people make the mistake of stating the Alternative Hypothesis as: *[Math Processing Error]* which says that every mean differs from every other mean. This is a possibility, but only one of many possibilities. To cover all alternative outcomes, we resort to a verbal statement of ‘not all equal’ and then follow up with mean comparisons to find out where differences among means exist. In our example, this means that fertilizer 1 may result in plants that are really tall, but fertilizers 2, 3 and the plants with no fertilizers don’t differ from one another. A simpler way of thinking about this is that at least one mean is different from all others.

**Step 3: Set ***[Math Processing Error]*

If we look at what can happen in a hypothesis test, we can construct the following contingency table:

In Reality |
||

Decision |
H_{0} is TRUE |
H_{0} is FALSE |

Accept H_{0} |
OK | Type II Error β = probability of Type II Error |

Reject H_{0} |
Type I Error α = probability of Type I Error |
OK |

You should be familiar with type I and type II errors from your introductory course. It is important to note that we want to set *[Math Processing Error]* before the experiment (*a-priori*) because the Type I error is the more ‘grevious’ error to make. The typical value of *[Math Processing Error]* is 0.05, establishing a 95% confidence level. **For this course we will assume ***[Math Processing Error]***=0.05.**

**Step 4: Collect Data**

Remember the importance of recognizing whether data is collected through an experimental design or observational.

**Step 5: Calculate a test statistic**

For categorical treatment level means, we use an *F* statistic, named after R.A. Fisher. We will explore the mechanics of computing the *F*statistic beginning in Lesson 2. The *F* value we get from the data is labeled *F*_{calculated}.

**Step 6: Construct Acceptance / Rejection regions**

As with all other test statistics, a threshold (critical) value of *F* is established. This *F* value can be obtained from statistical tables, and is referred to as *F*_{critical} or *[Math Processing Error]*. As a reminder, this critical value is the minimum value for the test statistic (in this case the *F* test) for us to be able to reject the null.

The *F* distribution, *[Math Processing Error]*, and the location of Acceptance / Rejection regions are shown in the graph below:

**Step 7: Based on steps 5 and 6, draw a conclusion about H**_{0}

_{0}

If the *F*_{calculated} from the data is larger than the Fα, then you are in the Rejection region and you can reject the Null Hypothesis with (1-α) level of confidence.

Note that modern statistical software condenses step 6 and 7 by providing a *p*-value. The *p*-value here is the probability of getting an *F*_{calculated} even greater than what you observe. If by chance, the *F*_{calculated }= *[Math Processing Error]*, then the *p*-value would exactly equal to α. With larger *F*_{calculated} values, we move further into the rejection region and the *p-*value becomes less than α. So the decision rule is as follows:

If the *p-*value obtained from the ANOVA is less than α, then Reject H_{0} and Accept H_{A}.

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