Conducting hypothesis tests for a proportion with summary data

This tutorial covers the steps for calculating hypothesis tests for a single proportion in StatCrunch.
While this tutorial uses summary data, see Conducting hypothesis tests for a proportion with raw data to compute one-sample proportion results with raw data.

Performing a one-sample proportion hypothesis test

If the coin used is "fair", the proportion of of heads the coin will produce over a very long run of flips should be 0.5. A coin was flipped 50 times, resulting in 31 heads and 19 tails. Do the 50 outcomes in this short run of flips suggest the coin is unfair? To conduct the appropriate hypothesis test, choose the Stat > Proportion Stats > One Sample > With Summary menu option. In StatCrunch, a success is used to define the outcome of interest. In this case, consider a head result to be a success. Set # of successes to be 31, and set # of observations to be 50.
Under Perform, the default Hypothesis test for p is selected. Enter 0.5 for the null value of the proportion, and click Compute!. The hypothesis test output includes the observed sample proportion of heads, the test statistic (labeled as Z-Stat) and the P-value.

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