Question

Claine A majority of adults would erase all of their personal inlormation onine if they oould. A sohware fim survey of 662 randomly solocted aduls showed that 61% of them would erise all of their personal information online if they could. Complete parts (a) and (b) below. a. Express the original claim in symbolic form. Let the parameter represent the adults that would erase their personal information. (Type an integer or a decimal. Do not reund.) b. Identify the null and alternative hypotheses. (Type integers or decimals. Do not round.) a. Express the original claim in symbolic form. Let the parameter represent the adults that would erase their personal information. jer or a decimal. Do not round.) null and altemative hypotheses. P a 3 or decimals. Do not round.) μ a. Express the original claim in symbolic form. Let the parameter represent the adults that would erase their personal irformat (Type 2 decimal. Do not round.) b. Ider id alternative hypotheses. H 0 ​ :[= H 1 ​ := (Type cimals. Do not round.) b. Identify the null and altemative hypotheses. D. Identiry the null and altemative hypotheses. b. Identify the null and altemative hypotheses. lecimals. Do not round.) b. Identify the null and altemative hypotheses. (Type intes ils. Do not round.)

Answer 1

The null hypothesis (H0) states there is no effect or no difference and is the claim tested against evidence. The alternative hypothesis (H1) is what is suspected to be true. In a scenario with 9.5% known proportion, if 7 out of 100 people in a town suffer from a disease, the null hypothesis would be p = 0.095 and the alternative would be p < 0.095, with the point estimate being the sample proportion of 0.07.

Step-by-step explanation:

In statistical hypothesis testing, we use the null hypothesis (H0) and alternative hypothesis (H1 or Ha) to compare a sample statistic against a population parameter. The null hypothesis often states that there is no effect or no difference, and it is the hypothesis that we are trying to find evidence against. The alternative hypothesis, on the other hand, is what we suspect might be true or hope to provide evidence for.

Let's consider a scenario where researchers have published an article stating that 9.5 percent of American adults suffer from a specific type of disease. If you conduct a survey of 100 people and only seven people suffer from this disease, and you want to test if the proportion in the town is less than 9.5 percent, the null and alternative hypotheses would be set up as follows:

• Null hypothesis (H0): p = 0.095 (The proportion of people with the disease is 9.5%)
• Alternative hypothesis (H1): p < 0.095 (The proportion of people with the disease is less than 9.5%)

The point estimate for this confidence interval would be the sample proportion, which is 7/100 or 0.07. The error bound will depend on the desired confidence level and the sample data. If you are creating a hypothesis test or confidence interval, you will need to perform additional calculations to determine the error bound and to draw conclusions based on the statistical evidence.