An article reports that blue eyed people earn less than brown eyed people, with these numbers: average blue-eyed salary $35,000, average brown-eyed salary $37,000, p-value 0.45. Based on that reported p-value, and using the common definition of "statistical significance," which is the case?

There is not enough evidence to support the claim that blue eyed people earn less than brown eyed people.

Step-by-step explanation:

In this case, we need to test whether the blue eyed people earn less than brown eyed people.

Let sample 1 denote the salary of blue eyed people and sample 2 denote the salary of brown eyed people.

The hypothesis to test the claim can be defined as follows:

H₀: The average salary of blue eyed people is not less than that for brown eyed people, i.e. μ₁ - μ₂ ≥ 0.

Hₐ: The average salary of blue eyed people is less than that for brown eyed people, i.e. μ₁ - μ₂ < 0.

It is provided that the p-value of the test is, p-value = 0.45.

The decision rule is:

If the p-value of the test is less than the significance level then the null hypothesis will be rejected and vice-versa.

The p-value of the test is 0.45.

The p-value of the test is very large for all the commonly used significance level. The null hypothesis will not be rejected.

Thus, it can be concluded that there is not enough evidence to support the claim that blue eyed people earn less than brown eyed people.

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step-by-step explanation:

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