One decision that may be relevant to someone involved in flipping cars could be related to the profitability of purchasing and fixing up a particular car model. A hypothesis that could be formulated to test this decision is as follows:
- H0: The profit earned from flipping a car is less than or equal to $5,000.
- Ha: The profit earned from flipping a car is greater than $5,000.
The decision-maker would need to gather a sample of data from flipping the specific automobile model, compute the sample mean profit, and compare it to the $5,000 hypothesized mean profit using a statistical test, such as a one-sample t-test. The decision-maker would reject the null hypothesis if the test statistic fell within the rejection zone, which is established by the significance level and degrees of freedom, and conclude that the mean profit from flipping the automobile model exceeds $5,000. The decision-maker would fail to reject the null hypothesis and conclude that there is insufficient evidence to show that the mean profit from flipping the automobile model is larger than $5,000 if, on the other hand, the test statistic falls within the non-rejection zone.
Importantly, the hypothesis testing should take into consideration two types of errors. If the decision-maker rejects the null hypothesis even though it is true, a type I error will happen (Kaliyadan & Kulkarni, 2019). This would entail that they draw the incorrect conclusion that the mean profit is more than $5,000 and invest in an unprofitable automobile model as a result. If the decision-maker accepts the null hypothesis even though it is incorrect, this would constitute a Type II error (Kaliyadan & Kulkarni, 2019). By assuming that the mean profit is $5,000 or less when it is actually larger, they risk missing out on a lucrative opportunity.
The decision-maker would need to consider both the practical value and statistical significance of the conclusions. Even if the null hypothesis is rejected, the expenditure could not be worthwhile if the difference in mean profit is too small to offset the expenses and labor required to flip the car model.
Reference
Kaliyadan, F., & Kulkarni, V. (2019). Types of variables, descriptive statistics, and sample size. Indian dermatology online journal, 10(1).