Question

In: Statistics and Probability

A tax preparation firm claims that the average savings from their tax review is more than...

A tax preparation firm claims that the average savings from their tax review is more than $2,500. if we wish to evaluate this claim through hypothesis testing the proper hypothesis statement is

Ho: u (=, >, <, >=, <=, not=) 2500

H1: u (=, >, <, >=, <=, not=) 2500

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Expert Solution

Answer :

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Population parameter : Average saving for tax reviews : ;

Claim : that the average savings from their tax review is more than $2,500;

The null and alternative hypotheses are two mutually exclusive statements about a population. A hypothesis test uses sample data to determine whether to reject the null hypothesis.

Null hypothesis (H0)

The null hypothesis states that a population parameter (such as the mean, the standard deviation, and so on) is equal to a hypothesized value. The null hypothesis is often an initial claim that is based on previous analyses or specialized knowledge.

Alternative Hypothesis (H1)

The alternative hypothesis states that a population parameter is smaller, greater, or different than the hypothesized value in the null hypothesis. The alternative hypothesis is what you might believe to be true or hope to prove true.

One-sided and two-sided hypotheses

The alternative hypothesis can be either one-sided or two sided.

Two-sided

Use a two-sided alternative hypothesis (also known as a nondirectional hypothesis) to determine whether the population parameter is either greater than or less than the hypothesized value. A two-sided test can detect when the population parameter differs in either direction, but has less power than a one-sided test.

One-sided

Use a one-sided alternative hypothesis (also known as a directional hypothesis) to determine whether the population parameter differs from the hypothesized value in a specific direction. You can specify the direction to be either greater than or less than the hypothesized value. A one-sided test has greater power than a two-sided test, but it cannot detect whether the population parameter differs in the opposite direction.


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