F value indicates the total sterilization effect under certain sterilization conditions. Usually, the sterilization time at different temperatures is 120℃, which is equivalent to 120℃. What needs special attention is that it does not refer to the time spent by workers in actual operation, but a theoretical conversion time.

P value is an index to measure the difference between the control group and the experimental group. * indicates that the P value is less than 0.05, indicating that the difference between the two groups is significant, and * * indicates that the P value is less than 0.0 1, indicating that the difference between the two groups is extremely significant.

This can be counted by SPSS. According to your description, the independent variable should be the sex of the fruit fly (female or male), the dependent variable should be the life span, the independent variable is a nominal variable, and the dependent variable is a continuous variable, so the result can be obtained by one-way analysis of variance.

In addition, in statistical interpretation, generally do not look at the F value, only need to look at the P value. But when writing a paper, you should write out the F value and put the P value in brackets.

R A Fisher (1890-1962), as the founder of hypothesis testing theory, first put forward the concept of P value in hypothesis testing. He believes that hypothesis testing is a procedure through which researchers can form judgments on a general parameter.

In other words, he believes that hypothesis testing is a form of data analysis and subjective information that people add in their research. (At that time, this view was opposed by Naiman-Pearson, who believed that hypothesis testing was a method, and decision makers could make a clear choice between the two possibilities while controlling the probability of error.

There is a long and painful debate between these two methods. Although Fisher's views are also opposed by modern statisticians, he has made great contributions to the development of modern hypothesis testing. )

Fisher's specific approach is:

Suppose the value of a parameter.

Select a test statistic (such as Z-statistic or Z-statistic). When the assumed parameter is true, its distribution should be completely known.

Select a random sample from the research population, calculate the value of the test statistic, and calculate the significance level of the probability p value or the observed value, that is, the probability that the test statistic is greater than or equal to the actual observed value under the premise of being true. ?

If p

If 0.0 1

If p value >; 0.05, indicating that the results are more inclined to accept the assumed parameter values.

However, in those days, it was not easy to calculate P value because of hardware problems, so people adopted statistical test method, which is the first method we learned to compare T value with T critical value. The statistical test method is to determine the significance level α before the test, that is to say, to determine the rejection domain in advance.

However, if the choice is the same, the reliability of all inspection conclusions is the same, and it is impossible to give an accurate measure of the inconsistency between the observed data and the original hypothesis. As long as the statistics fall in the rejection domain, the assumed results are all the same, that is, the results are significant. But in fact, the position of statistical data in the denial domain is different, and the actual meaning is also very different.

Therefore, with the development of computer, the calculation of P value is no longer a difficult problem, which makes P value one of the most commonly used statistical indicators.