Hypotheses Test: H0:\mu = 5 vs Ha \mu >/< 5

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In summary, if the water is at level 5, then it is safe. If the water is not at level 5, then it is unsafe.
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needhelp83
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Let [tex]\mu[/tex] denote the true average radioactivity level (picocuries per liter). The value 5 pCi/L is considered the dividing line between safe and unsafe water. Would you recommend testing H0:[tex]\mu[/tex] = 5 versus Ha: [tex]\mu[/tex] > 5 or H0:[tex]\mu[/tex] = 5 versus Ha: [tex]\mu[/tex] < 5? Explain your reasoning. (Hint: Think about the consequences of a type I and type II error for each possibility)

Attempted solution:
I would think about running the test of H0:[tex]\mu[/tex] = 5 versus Ha: [tex]\mu[/tex] > 5 where my Type I error would reject my null hypothesis when it is actually true. With this approach, I am more on the safe side and this would make the water unsafe. If I encountered the Type II error the null hypothesis wouldn't be rejected when it is actually false. This again, would be playing it on the safe side if it wasn't greater than 5.

If I used H0:[tex]\mu[/tex] = 5 versus [tex]\mu[/tex] < 5, type I error would be say the water is not at level 5 when it is actually 5. This would then mean water is less than 5. Type II error would fail to reject null hypothesis when it is false and say that the water is less than 5 when it is truly 5.
 
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Just a thought here: One of the tests is "assume water is safe until we prove it unsafe." The other is "assume water is unsafe until we prove it safe." Which is which?
 
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Billy Bob said:
Just a thought here: One of the tests is "assume water is safe until we prove it unsafe." The other is "assume water is unsafe until we prove it safe." Which is which?

I would think about running the test of H0:LaTeX Code: \\mu = 5 versus Ha: LaTeX Code: \\mu > 5 where my Type I error would reject my null hypothesis when it is actually true. With this approach, I am more on the safe side because you would actually do something about the water if it was unsafe. Since Type I errors occurs more often than, the rejection of Ho would hopefully not happen.

It is no need to test if the water is Ho:LaTeX Code: \\mu = 5 versus Ha: LaTeX Code: \\mu < 5 because we are more concerned with figuring out if the water is safe. If we do this test, we aren't necessarily proving if the water is unsafe or not.

Does this approach sound better?
 

1. What is a hypotheses test?

A hypotheses test is a statistical method used to determine if there is enough evidence to reject or fail to reject a null hypothesis. The null hypothesis is a statement that there is no significant difference between two groups or variables being compared. The alternative hypothesis is a statement that there is a significant difference.

2. What does H0:μ = 5 vs Ha:μ > 5 mean?

H0:μ = 5 vs Ha:μ > 5 is a specific type of hypotheses test where the null hypothesis (H0) is that the population mean (μ) is equal to 5, and the alternative hypothesis (Ha) is that the population mean is greater than 5. This means we are trying to determine if there is enough evidence to support the claim that the population mean is greater than 5.

3. How is a hypotheses test conducted?

A hypotheses test involves several steps. First, a sample is collected and relevant data is gathered. Then, a test statistic is calculated, which is a value that measures the difference between the sample data and the null hypothesis. Next, a p-value is calculated, which indicates the probability of obtaining the observed results if the null hypothesis is true. If the p-value is less than the chosen significance level (usually 0.05), the null hypothesis is rejected and the alternative hypothesis is supported. If the p-value is greater than the significance level, the null hypothesis is not rejected.

4. What is the significance level in a hypotheses test?

The significance level, also known as alpha (α), is the probability of rejecting the null hypothesis when it is actually true. This is typically set at 0.05 or 5%, but it can vary depending on the specific study or experiment. A lower significance level means that the evidence needed to reject the null hypothesis is stronger, while a higher significance level means that weaker evidence is needed to reject the null hypothesis.

5. What are the types of errors that can occur in a hypotheses test?

There are two types of errors that can occur in a hypotheses test. Type I error, also known as a false positive, occurs when the null hypothesis is rejected when it is actually true. This means that a significant difference is found when there is actually no difference. Type II error, also known as a false negative, occurs when the null hypothesis is not rejected when it is actually false. This means that no significant difference is found when there is actually a difference. The significance level chosen for the test can help control the likelihood of these errors occurring.

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