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Hypothesis testing is an important approach in statistics. This method allows assessing two opposite hypotheses in order to determine which of them might be supported by sample data. Hypothesis tests let statists reveal significant information. The main goal of this paper is to examine practical applications of hypothesis testing.
A restaurant in a small French town posted an additional advertisement on the local website in order to attract more clients. Over the next two summer months, the number of customers increased by 12 percent. The restaurant manager believes that this improvement is the result of an internet advertisement. Therefore, this situation leads to the next question: Did the additional advertisement cause an increase in the number of customers in the restaurant? Therefore, it is important to determine whether the advertisement lifetime should be extended or not. In order to answer this question without hypothesis testing, it is necessary to conduct a thorough analysis and carefully consider all alternative factors that could have led to similar outcomes. However, this approach will provide much better results if it is combined with hypothesis testing. It is necessary for determining all potential factors that are pertinent to the issue.
The hypothesis test will consist of four steps. First, it is necessary to state the null and alternative hypotheses, which exclude one another (“Hypothesis testing,” n.d.). Second, an analysis plan should be developed. This plan has to describe how to apply sample data when assessing the null hypothesis. Third, sample data should be analyzed. Fourth, the final step is to interpret results and, if necessary, reject the null hypothesis.
In this case, the null hypothesis is the advertisement attracted more attention to potential customers, which eventually led to the increase of their number. However, there is an alternative factor that could have brought about such results. The alternative hypothesis is during summer months the town becomes more crowded, thus the growing number of customers was caused by tourists who came to the town on their holidays and most of whom did not visit the local website. In order to check these hypotheses, a normal distribution type test should be carried out.
To implement the analysis plan, it is necessary first to describe the required data and second, to apply them. The null hypothesis might be tested by finding the ratio of the population in the town during summer months to the population during the rest of the year. These data already exist and are kept in the town archive, thus there is no need to collect it.
The analysis of these data should reveal if the increase in the number of customers is similar to the population growth during summer months in percentage terms. It is also necessary to compare these data to the same parameters for the previous years. This test will allow concluding which factor is more likely to cause such results. However, it is necessary to explain to the management of the restaurant the significance of the test. The advertisement might be ineffective, and thus, the expenses on it are unnecessary. In addition, the test might demonstrate that the management needs to employ another marketing strategy. Therefore, the major drawback of not using a hypothesis test is over-expenditure on the potentially ineffective advertisement.
In conclusion, hypothesis testing is an important method that helps to determine the effectiveness of different solutions. The main elements of this approach are null and alternative hypotheses. These are two mutually exclusive statements that are checked through hypothesis testing. Also, it requires collecting and analyzing sample data in order to prove or refute one of these hypotheses. This method is widely used by various statists as it is one of the most effective statistical approaches in data analysis.
Hypothesis testing. (n.d.). Web.