Which best describes bias, if any, from the sample surveyed?
2 answers:
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Answer:
Null Hypothesis, :
= 136 mm Hg
Alternate Hypothesis, :
136 mm Hg
In this context type I error is rejecting that the μ is equal to 136 mm Hg when in fact μ is equal to 136 mm Hg.
Step-by-step explanation:
We are given that the mean systolic blood pressure, μ, of CEOs of major corporations is different from 136 mm Hg, which is the value reported in a possibly outdated journal article.
He measures the systolic blood pressures of a random sample of CEOs of major corporations and finds the mean of the sample to be 126 mm Hg and the standard deviation of the sample to be 18 mm Hg.
So, Null Hypothesis, :
= 136 mm Hg {means that the mean systolic blood pressure, μ, of CEOs of major corporations is 136 mm Hg}
Alternate Hypothesis, :
136 mm Hg {means that the mean systolic blood pressure, μ, of CEOs of major corporations is 136 mm Hg}
Type I error states that the null hypothesis is rejected when in fact the null hypothesis was true. So, in this context type I error is rejecting that the μ is equal to 136 mm Hg when in fact μ is equal to 136 mm Hg.
Suppose the researcher decides not to reject the null hypothesis. It means the researcher is making a type I error.