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To obtain the exact p-value, use statistical software.

Caution: If you are running an F Test in Excel, make sure your 1 is smaller than variance 2. This “quirk” can give you an incorrect f ratio if you put the variances in the wrong place. See the bottom of this article for an example:

Performs an F test to compare the variances of two samples from normalpopulations.

The F statistic must be used in combination with the p value when you are deciding if your overall results are significant. Why? If you have a significant result, it doesn’t mean that all your variables are significant. The statistic is just comparing the joint effect of all the variables together.

the value of the F test statistic.

In general, if your calculated F value in a test is larger than your F statistic, you can . However, the statistic is only one measure of significance in an F Test. You should also consider the . The is determined by the F statistic and is the probability your results could have happened by chance.

for testing homogeneity of variances inmore than two samples from normal distributions; and for two rankbased (nonparametric) two-sample tests for difference in scale.

The test statistic is (F^*= rac).

The F value in is a tool to help you answer the question “Is the between the of two populations ?” The F value in the test also determines the ; The P value is the probability of getting a result at least as extreme as the one that was actually observed, given that the is true.

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 Fcalculated.

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How does a negative z-score effect the critical region of the test?

It’s good science to let people know if your study results are solid, or if they could have happened by chance. The usual way of doing this is to test your results with a . A p value is a number that you get by running a hypothesis test on your data. A P value of 0.05 (5%) or less is usually enough to claim that your results are repeatable. However, there’s another way to test the validity of your results: Bayesian Hypothesis testing. This type of testing gives you another way to test the strength of your results.

What descriptive and inferrential statistics to use

Traditional testing (the type you probably came across in elementary stats or AP stats) is called Non-Bayesian. It is how often an outcome happens over repeated runs of the experiment. It’s an objective view of whether an experiment is repeatable.
Bayesian hypothesis testing is a subjective view of the same thing. It takes into account how much faith you have in your results. In other words, would you wager money on the outcome of your experiment?

If one were testing H(0): MU = 1 vs.


Bayesian hypothesis testing helps to answer the question: Can the results from a test or survey be repeated?
Why do we care if a test can be repeated? Let’s say twenty people in the same village came down with leukemia. A group of researchers find that cell-phone towers are to blame. However, a second study found that cell-phone towers had nothing to do with the cancer cluster in the village. In fact, they found that the cancers were completely random. If that sounds impossible, it actually can happen! Clusters of cancer can happen . There could be many reasons why the first study was faulty. One of the main reasons could be that they just didn’t take into account that sometimes things happen randomly and we just don’t know why.

The descriptive significance level of the test is: a.

Traditional testing (Non Bayesian) requires you to repeat sampling over and over, while Bayesian testing does not. The main different between the two is in the first step of testing: stating a probability model. In Bayesian testing you add prior knowledge to this step. It also requires use of a , which is the given to a after all the evidence is considered.

the Type I error probability of the test.

Sample problem: A sample of 200 people has a mean age of 21 with a population standard deviation (σ) of 5. Test the hypothesis that the population mean is 18.9 at α = 0.05.

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