The alternative hypothesis states that the mean proportion is greater than 0.35 as we have had a sample proportion of 0.42. The null hypothesis will then state that the proportion is maximum 0.35 despite the new finding of 0.42. In our voting example, say we wish to test if the proportion mean is greater than the assumed 0.35. Our null hypothesis states that there is no change. Let’s test it proportion hypothesis testing: Is this new finding of the 42% significant and can the proportion mean therefore be expected to be larger than the assumed 0.35? It is predicted that candidate C will get 35% of the votes, but running a sample survey of 100 voters, Candidate C becomes 42% of the predicted votes. I will use the following example to run through the procedure:Ī larger organization is due to elect a new board member. The procedure is for proportion hypothesis testing is the same as described in hypothesis testing: Procedure for proportion hypothesis testing It answers to the question if the new findings are significant with which we can reject the null hypothesis. It tests new findings against the assumed proportion estimate.Proportion hypothesis testing is applied to test an assumption for a population proportion.
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