![]() We never say that we “accept” the null hypothesis. If the P-value is greater than the significance level, we say we “fail to reject” the null hypothesis.If the P-value is less than or equal to the significance level, we reject the null hypothesis and accept the alternative hypothesis instead. A small P-value says the data is unlikely to occur if the null is true.If the alternative hypothesis is not equal to, the P-value is equal to double the tail area beyond the test statistic. If the alternative hypothesis is less than, the P-value is the area to the left of the test statistic. If the alternative hypothesis is greater than, the P-value is the area to the right of the test statistic.You can use a standard normal table (or Z-table) or technology (such as the simulations on the second page of this topic) to find the P-value. Use the test statistic, together with the alternative hypothesis to determine the P-value.Determine the test statistic which is the z-score for the sample proportion.Additionally, we need to check whether the sample proportion can be np ≥ 10 and n(1 − p) ≥ 10. Since the hypothesis test is based on probability, random selection or assignment is essential in data production. The alternative hypothesis is the competing claim that the parameter is less than, greater than, or not equal to p 0.The null hypothesis is a hypothesis that the proportion equals a specific value, p 0.The hypotheses are claims about the population proportion, p.In this section, we looked at the four steps of a hypothesis test as they relate to a claim about a population proportion. Whether such a P-value is sufficient for us to reject a particular null hypothesis ultimately depends on the risk of making the wrong decision and the extent to which the hypothesized effect might contradict our prior experience or previous studies. In either case, the study results are roughly 5% likely by chance if there’s no actual effect. There isn’t much meaningful difference, for instance, between the P-values 0.049 and 0.051, and it would be foolish to declare one case definitely a “real” effect and the other case definitely a “random” effect. But it’s important to remember that there is really a continuous range of increasing confidence toward the alternative hypothesis, not a single all-or-nothing value. The idea of selecting some sort of relatively small cutoff was historically important in the development of statistics. Later, these same 5% and 1% levels were used by other people, in part just because Fisher was so highly esteemed. When Ronald Fisher (one of the founders of modern statistics) published one of his tables, he used a mathematically convenient scale that included 5% and 1%. ![]() It is largely due to just convenience and tradition. You may wonder why 5% is often selected as the significance level in hypothesis testing and why 1% is also a commonly used level. One More Note about P-Values and the Significance Level Looking at the table, the p-value of 0.354 is greater than the level of significance of 0.05 (5%), we fail to reject the null hypothesis.\) Using the P-value Formula Table, Check if the Hypothesis is Rejected or not when the P-value is 0.354 with 5% Level of Significance. It indicates the null hypothesis is very likely. It indicates the null hypothesis is very unlikely. \(Z = \frac=\) assumed population proportion in the null hypothesis ![]() The formula for the calculation for P-value is: The level of significance(α) is a predefined threshold that should be set by the researcher. P-value always only lies between 0 and 1. P-value is an important statistical measure, that helps to determine whether the hypothesis is correct or not. The smaller the P-value, the stronger is the evidence in favor of the alternative hypothesis given observed frequency and expected frequency. The P-value formula is used as an alternative to the rejection point to provide the least significance for which the null hypothesis would be rejected. The P-value represents the probability of occurrence of the given event. P-value defines the probability of getting a result that is either the same or more extreme than the other actual observations. The P-value formula is short for probability value.
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