Chapter 7: Inference for Means
Chapter 8: Inference for Means
8.1 Inference for Proportions
Overview
In this section we consider inference about a population proportion [pic] from an SRS of size n based on the sample proportion [pic] where X is the number of successes in the sample.
|Large-Sample Significance test for a Population Proportion |
Draw an SRS of size n from a large population with unknown proportion p of successes.
To test the hypothesis [pic], compute the z-statistic
[pic]
In terms of a standard normal random variable Z, the appropriate P-value for a test of [pic] against
[pic] is P([pic])
[pic] is P([pic])
[pic] is 2P([pic])
Example 1. The French naturalist Count Buffon once tossed a coin 4040 times and obtained 2048 heads. This is a binomial experiment with n=4040. The sample proportion is
[pic]
If Buffon’s coin was balanced, then the probability of obtaining heads on any toss is 0.5. To assess whether the data provide evidence that the coin was not balanced, we test
[pic]
[pic]
The test statistic is
[pic]
Figure 1. illustrates the calculation of the P-value. From Table A we find
[pic].
The probability in each tail is 1-0.8106 = 0.1894, and the P-value is [pic]. Since P-value is larger then [pic], we do not reject [pic] at the level [pic].
[pic]
Figure 1. The P-value for Example 1.
Example 2. A coin was tossed n=4040 times and we observed X=1992 tails. We want to test the null hypothesis that the coin is fair- that is, that the probability of tails is 0.5. So [pic] is the probability that the coin comes up tails and we test
[pic]
[pic]
The test statistic is
[pic]
Using Table A, we find that [pic]. Since P-value is larger then [pic], we do not reject [pic] at the level [pic].
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