This page calculates (0-1) the interval estimate of the distribution parameter, which is actually the interval estimate of the probability p of an event A. Event A can be any event, for example, taking a product, it is a good product, or you can find a job, hired by the hired party, the next game, the result is won, and so on. Usually, it is necessary to repeat n independent tests repeatedly. If event A occurs m times, then m/n is the frequency at which A occurs. If n is large, it can be approximated as the probability of occurrence of event A. Of course, this is considered inaccurate, and the frequency can only be considered as a probability when the number of trials is infinite. But in fact we can't do infinite trials, so the real probability we don't know, However, with the interval estimation method, in the case of artificially giving a confidence probability of 1-α (α is a significant factor) close to 1, the degree of reliability with this confidence probability can be based on the frequency of occurrence. Estimate an interval (p1, p2), The probability p of occurrence of A definitely falls within this interval. Of course, this interval will become smaller as the number of trials increases. Be careful not to confuse the "confidence probability" 1-α with the probability p we want to estimate.
Enter the total number of n-tests in the input box below, m-the number of times the event A occurs, click the " Start Calculation" button to calculate. The confidence probability can be selected in the list on the right.
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