(b) A random sample of size 30 is taken from a population that is assumed to have a standard deviation of 5. The standard deviation of the sample mean is 5/30. (c) A study with = 45 reports statistical significance for H : μ > 50. (d) A researcher tests the hypothesis Ho: μ = 350 and concludes that the population mean is equal to 350.

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**(b)** A random sample of size 30 is taken from a population that is assumed to have a standard deviation of 5. The standard deviation of the sample mean is \( \frac{5}{\sqrt{30}} \).

**(c)** A study with \( \bar{x} = 45 \) reports statistical significance for \( H_a: \mu > 50 \).

**(d)** A researcher tests the hypothesis \( H_0: \mu = 350 \) and concludes that the population mean is equal to 350.
Transcribed Image Text:**(b)** A random sample of size 30 is taken from a population that is assumed to have a standard deviation of 5. The standard deviation of the sample mean is \( \frac{5}{\sqrt{30}} \). **(c)** A study with \( \bar{x} = 45 \) reports statistical significance for \( H_a: \mu > 50 \). **(d)** A researcher tests the hypothesis \( H_0: \mu = 350 \) and concludes that the population mean is equal to 350.
### 6.52 What's Wrong?

Here are several situations where there is an incorrect application of the ideas presented in this section. Write a short paragraph explaining what is wrong in each situation and why it is wrong.

**(a)** A researcher tests the following null hypothesis: \( H_0: \bar{x} = 23 \). 

---

### Explanation:

In situation (a), the null hypothesis \( H_0: \bar{x} = 23 \) is incorrect because the symbol \( \bar{x} \) typically represents the sample mean, which is a statistic calculated from the data. In hypothesis testing, the null hypothesis should be stated in terms of a population parameter, such as the population mean \( \mu \). A correct formulation of the null hypothesis might be \( H_0: \mu = 23 \). This distinction is important because hypothesis testing is used to make inferences about population parameters, not sample statistics.
Transcribed Image Text:### 6.52 What's Wrong? Here are several situations where there is an incorrect application of the ideas presented in this section. Write a short paragraph explaining what is wrong in each situation and why it is wrong. **(a)** A researcher tests the following null hypothesis: \( H_0: \bar{x} = 23 \). --- ### Explanation: In situation (a), the null hypothesis \( H_0: \bar{x} = 23 \) is incorrect because the symbol \( \bar{x} \) typically represents the sample mean, which is a statistic calculated from the data. In hypothesis testing, the null hypothesis should be stated in terms of a population parameter, such as the population mean \( \mu \). A correct formulation of the null hypothesis might be \( H_0: \mu = 23 \). This distinction is important because hypothesis testing is used to make inferences about population parameters, not sample statistics.
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