Example: Assume that adults were randomly selected for a poll. They were asked if they "favor or oppose using federal tax dollars to fund medical research using stem cells obtained from human embryos.” Of those polled, 480 were in favor and 394 were opposed. A politician claims that people don’t really understand the stem cell issue and their responses to such questions are random responses equivalent to a coin toss. Use a 0.01 significance level to test the claim that the proportion of subjects who respond in favor is equal to 0.5. What does the result suggest about the politician’s claim? Following the hypothesis test steps, let’s first identify the claim and the null and alternative hypotheses: For this example, the original claim is that the proportion of the subjects who responded in favor is equal to 0.5 (half – since the politician compared their responses as equivalent to a coin toss). The symbolic form of the politician’s claim can be expressed as p = 0.5. Also notice that this hypothesis test is about a proportion – it will be very important for you to identify in each question if you are conducting a hypothesis test about a population proportion, mean, or standard deviation so that you know which StatCrunch directions to follow, and how to analyze your results. In this project, you will only be responsible for testing claims about proportions and means. The null hypothesis (H0) is the statement that the value of a population parameter is EQUAL (=) to a claimed value, and the alternative hypothesis (H1) is a statement that the parameter has a value that somehow differs from the null hypothesis (<, >, or ≠). For this example, our null and alternative hypotheses are: H0: p = 0.5 H1: p ≠ 0.5 Is this test two-tailed, left-tailed, or right-tailed? a. Now that we have identified the alternative hypotheses, we can answer this question. Since our alternative hypothesis contains the symbol ≠, this will be a two- tailed test, meaning that the critical region is in both tails. (Note: If the alternative hypothesis contains < or >, the test is either left-tailed or right-tailed – see section 8.1 for further explanation.)
Example:
Assume that adults were randomly selected for a poll. They were asked if they "favor or oppose using federal tax dollars to fund medical research using stem cells obtained from human embryos.” Of those
polled, 480 were in favor and 394 were opposed. A politician claims that people don’t really understand the stem cell issue and their responses to such questions are random responses equivalent to a coin toss. Use a 0.01 significance level to test the claim that the proportion of subjects who respond in favor is equal to 0.5. What does the result suggest about the politician’s claim?
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Following the hypothesis test steps, let’s first identify the claim and the null and alternative hypotheses:
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For this example, the original claim is that the proportion of the subjects who responded in favor is equal to 0.5 (half – since the politician compared their responses as equivalent to a coin toss). The symbolic form of the politician’s claim can be expressed as p = 0.5.
-
Also notice that this hypothesis test is about a proportion – it will be very important for you to identify in each question if you are conducting a hypothesis test about a population proportion, mean, or standard deviation so that you know which StatCrunch directions to follow, and how to analyze your results. In this project, you will only be responsible for testing claims about proportions and means.
-
The null hypothesis (H0) is the statement that the value of a population parameter is EQUAL (=) to a claimed value, and the alternative hypothesis (H1) is a statement that the parameter has a value that somehow differs from the null hypothesis (<, >, or ≠). For this example, our null and alternative hypotheses are:
H0: p = 0.5
H1: p ≠ 0.5
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Is this test two-tailed, left-tailed, or right-tailed?
a. Now that we have identified the alternative hypotheses, we can answer this question. Since our alternative hypothesis contains the symbol ≠, this will be a two- tailed test, meaning that the critical region is in both tails. (Note: If the alternative hypothesis contains < or >, the test is either left-tailed or right-tailed – see section 8.1 for further explanation.)
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