数据显著性分析怎么做（spss显著性分析怎么做）

Yishuo school district (3) | SPSS statistical analysis (12) hypothesis test (I)

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Today, let's talk about a very important concept in Statistics – hypothesis testing. What is hypothesis testing? Hypothesis test, also known as "significance test", is a method in statistics to infer the population from samples according to certain assumptions. Generally speaking, it is to make some assumptions about the studied population first, and then conduct sampling research, so as to infer whether the assumptions should be rejected or accepted. If the sample data can not fully prove and support the hypothesis, the hypothesis should be rejected under certain probability conditions. On the contrary, if the sample data cannot fully prove and support the hypothesis, the original hypothesis cannot be overturned.

One of the bases for hypothesis testing is the principle of small probability events. In probability theory, we call events with a probability close to 0 as small probability events. Statistically, events with a probability below 0.01 or 0.05 are generally called small probability events. Small probability events can be regarded as events that cannot occur in a sampling. If an event is theoretically considered to be a small probability event when the original hypothesis is established, If it happens in the sampling, we will overturn the original hypothesis and adopt another alternative hypothesis.

Here we also need to explain some concepts about hypothesis testing. Statistics include original hypothesis and alternative hypothesis. The original hypothesis refers to the tested hypothesis. We can choose to accept or reject the hypothesis through testing. Generally, we choose the hypothesis we want to reject as the original hypothesis. The alternative hypothesis is a hypothesis opposite to the original hypothesis. The alternative hypothesis is tenable only when the original hypothesis is rejected.

Forecast for next issue: in this section, we first understand what is hypothesis testing and what is statistical hypothesis. In the next issue, we will further understand the theoretical content of hypothesis testing. It includes what is the rejection domain, the two major errors of hypothesis testing, significance level, probability p value, etc.

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