The Importance of the Null Hypothesis
A hypothesis is a statementabout a population parameter that has to undergo a verification process to check its clarity or prove its efficacy in regard to its representation of that particular parameter. A hypothesis could either be null or alternate. “The null hypothesis is a statement that is not rejected unless the sample population being studied provides convincing evidence that the statement is not true.” The null hypothesis is important in that it represents the status quo, that is, the way things are already known, we can only make conclusions that things are different only after presenting evidence proving so. However, it has some limitations one is that it is difficult to make a precise quantification of some ideas, e.g., Peter is a better student than John, it also works on assumptions which could sometimes be misleading, and also difficult in testing complex patterns. In management, there are many questions that need to be answered empirically. For example, are people who work the night shift more productive than the day shift; or are stores located in the city more profitable than those in the suburbs.
Hypothesis tests can help answer the above questions. Without the tests, we can look at averages or just compare two numbers (people who worked at night produced 100 tools, the other 80 tools, so those who work at night are more productive). But if we test the two groups’ productivity using the hypothesis, we can confirm with certainty if the statement is true. In this case, the null hypothesis will be “people who work during the night shift are less productive than those who work during the day shift;” while the alternate hypothesis will be “people who work during the night shift are more productive than those who work during the day shift.” If the data collected does not support the null hypothesis, then it is rejected and the alternative one is adopted. Therefore, the conclusion is that productivity increases if people work at night. We can only know this by doing the test since our initial conclusion might have been based on a sampling error.
Hypothesis testing can assist the management to make appropriate conclusions. Null and alternate hypothesis testing procedures are widely used in the business and industrial sectors to make crucial decisions. You can use a hypothesis generation tool to come up with examples and run a few tests to ensure their value. Sectors that make use of these tests include marketing departments to research consumer behavior and produce appropriate marketing strategies. Developmental organizations also use null and alternate hypothesis testing to formulate economic models and market forecasts. The use of null and alternate hypothesis testing in these industries is warranted because it helps reduce uncertainties when it comes to making important decisions. Thus, the likelihood of decisions being succeeding is high because they are backed by solid data.