# why do you think hypothesis testing is important in statistics?

Question Description

For this week’s discussion, why do you think hypothesis testing is important in statistics? In your response, please connect your comments to the concept of null and alternative hypotheses and describe what these mean. Your initial response to the Discussion topic should be a minimum of 200 words. You should also provide at least two responses to your classmates that should be a minimum of 100 words. In your peer replies, you are encouraged to challenge responses to promote critical thinking on all sides of a discussion.

Classmate Post #1

Hypothesis testing is the process of using statistics to determine the probability that a specific hypothesis is true. It’s an essential procedure in statistics. A hypothesis test evaluates two mutually exclusive statements about a population to determine which statement is best supported by the sample data. A hypothesis predicts the relationship between two variables. Hypothesis testing is common in statistics as a method of making decisions using data. In other words, testing a hypothesis is trying to determine if your observation of some phenomenon is likely to have really occurred based on statistics. This test normally comes from a statistical standpoint. The null hypothesis always states that the population parameter is equal to the claimed value. The alternative hypothesis should be decided upon before collecting or looking at any data, so as not to influence the results.

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Classmate Post #2

Hello,

Hypothesis testing is important in statistics because it helps to draw conclusions and make decisions about the nature of populations. It is proof that your data is significant and didn’t occur by chance. A conclusion is determined by examining a sample of a population. A decision is made based on the tests between two hypotheses. Two exclusive statements are made about the population and the test determines which statement supports the data. The hypothesis or conclusion about the population can have two outcomes. On one end you have a null hypothesis and then the alternative hypothesis.

When testing a hypothesis you form your opinion on what you think is occurring in the population to validate that one group is different from the other. Your conclusion can either be a null hypothesis or an alternative hypothesis. A null hypothesis states that nothing happened or there wasn’t any differences noted or no cause and effect. The alternative hypothesis, which states that something happened and there are differences. With hypothesis testing, you are trying to prove that there was a change and you are trying to disprove the null hypothesis.

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