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How to compare two population proportions-Dummies?
In order to make this comparison, two independent (separate) random samples need to be selected, one from each population. The null hypothesis H 0 is that the two population proportions are the same; in other words, that their difference is equal to 0.
How to compare two populations from independent samples?
We consider each case separately, beginning with independent samples. As with comparing two population proportions, when we compare two population means from independent populations, the interest is in the difference of the two means.
Is the percentage of a population the same or different?
… Both percentages in the first cases are the same but a change of one person in each of the populations obviously changes percentages in a vastly different proportion. Should I take that into account when presenting the data?
How can we compare two types of samples?
The two types of samples require a different theory to construct a confidence interval and develop a hypothesis test. We consider each case separately, beginning with independent samples.
How many times can a sample be over sampled?
Growing literature states that the population must be at least ten or 20 times the size of the sample. This keeps each population from being over-sampled and causing incorrect results. Comparing two proportions, like comparing two means, is common.
Which is an example of comparing two proportions?
Additionally, most of our examples thus far have involved left tailed tests in which the alternative hypothesis involved H A: p < p 0 or right-tailed tests in which the alternative hypothesis involved H A: p > p 0. Here, let’s consider an example that tests the equality of two proportions against the alternative that they are not equal.
When to conduct a hypothesis test comparing two independent population proportions?
When conducting a hypothesis test that compares two independent population proportions, the following characteristics should be present: The two independent samples are simple random samples that are independent. The number of successes is at least five, and the number of failures is at least five, for each of the samples.