Hypothesis testing is a step-by-step process to determine whether a stated hypothesis about a given population is true. It is an important tool in business development. By testing different theories and practices, and the effects they produce on your business, you can make more informed decisions about how to grow your business moving forward. Hypothesis testing can keep you from wasting time on initiatives that have no effect on growing your business, and it can help you maximize your resources and manpower by focusing them toward measures that can produce the biggest effects. Once you understand how hypothesis testing works and the steps involved, it is easy to apply it to your business decisions.
Hypothesis Testing: The Basics
Hypothesis testing examines whether a hypothesis about a given population is true. It does so by reframing the supposition as a pair of opposing hypotheses. The first is called the null hypothesis. The null hypothesis means no effect, or no change was observed in the population that cannot be explained by random chance. Opposing the null hypothesis is the alternative hypothesis, which states that any change seen in the population was too improbable to be explained by random chance. Ultimately, the goal of hypothesis testing is to either accept the null hypothesis or reject it. This requires working your way through four steps to arrive at a conclusion.
Step One: State the Hypothesis
The first step of hypothesis testing is to state your hypothesis as a set of opposing theories, so only one can be right. For example, if you want to know whether Toronto residents’ IQs differ from Canadians in general, you might set your hypotheses up as follows: The null hypothesis is that no difference exists between Toronto residents’ IQs and Canadians’ IQs that cannot be explained by random chance. The alternative hypothesis is that the two sets of IQs differ. Once you decide on your hypothesis and frame it in the proper way, you can advance to the testing process.
Step Two: Set the Parameters
To determine if random chance was responsible for your test results, you have to define your threshold for random chance. This process is known as setting the level of statistical significance. For example, if you set the level of significance at 5%, the most common measure used in hypothesis testing, then what you’re looking to determine is whether, given the null hypothesis being true, the likelihood of obtaining your test results is less than 5%. For example, if the mean IQ in Canada is 102, and your test sample from Toronto has a mean of 107, and your calculation of statistical significance indicates only a 3% probability that random chance explains the difference, you would reject the null hypothesis and conclude that Toronto IQs are higher.
Step Three: Compute the Data
Once your hypothesis is in place and your parameters are set, you can gather the data you need and begin calculating it. Suppose your sample population from Toronto consists of 500 randomly selected adults. During step three of the hypothesis testing process , you would gather the IQs of these people and compare them to the IQs of Canadians at large. Using the statistical significance formula, you would calculate the probability that any difference seen is due to random chance.
Step Four: Analyze the Results
After gathering the necessary data, computing it, and measuring the level of statistical significance, it’s time to analyze the results by comparing the result to the threshold you set in step two. How your results compare to the parameters you set determine whether to accept the null hypothesis or reject it. If your result meets or exceeds the required level of statistical significance, then the null hypothesis is determined to be false. Otherwise, the null hypothesis is determined to be true.
Hypothesis Testing in Business Development
Hypothesis testing has many uses for helping you develop your business. Suppose you are training your outside sales force, and want to know whether a specific sales technique results in a higher close ratio than the methods currently employed by your company. To make this determination, you can take the same steps as outlined above for the Toronto IQ experiment. Your null hypothesis would be that the new technique has no effect on sales that isn’t explained by random chance, while your alternative hypothesis would be that the method does have an effect, whether positive or negative. If you conclude that the technique has an effect, and it is positive, then you can implement the new method with confidence, knowing it is likely to bring you results. Hypothesis testing sounds complicated, but it is a simple process when broken down into steps, and it can help you make better business decisions.