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The null hypothesis, denoted by H₀, is the claim that is initially assumed to be true (the 鈥減rior belief鈥� claim). The alternative hypothesis, denoted by H_a, is the assertion that is contradictory to H₀.

The null hypothesis will be rejected in favour of the alternative hypothesis only if sample evidence suggests that H₀ is false. If the sample does not strongly contradict H₀, we will continue to believe in the plausibility of the null hypothesis. The two possible conclusions from a hypothesis-testing analysis are then reject H0 or fail to reject H₀.

The interest of the potential customer is hypothesis \({H_0}:\mu = 750\) versus \({H_a}:\mu < 750\). At the significance level of \(0.05\) it can be concluded that the null hypothesis is to be reject. This is because the null hypothesis is rejected when \(z < - {z_\alpha }\). Since,

\(\begin{array}{l}{z_\alpha } = {z_{0.05}}\\ = 1.645\end{array}\)

And \(z = - 2.14\).

\(z < - {z_\alpha }\), the null hypothesis is rejected and the customer does not continue with the purchase.

However, at significance level of \(0.01\) the null hypothesis is not reject and the customers continue with the purchase. Since,

\({z_\alpha } = {z_{0.01}} = 2.33,\)and \(z = - 2.14\).

\(z < - {z_\alpha }\),so the null hypothesis is not rejected.

The \(0.01\) significance level and the customer should continue with the purchase.

Hence,

For significance level of \(0.05\) reject the null hypothesis.

For significance level of \(0.01\) do not reject the null hypothesis.