The simplest definition of a hypothesis is that it is an informed guess about something in your surroundings. Unlike other guesses, a hypothesis can be tested to prove whether or not it’s true.
In this case, assume that you want to implement new technology in the office because you believe that it will make your workers more productive. When formulating a hypothesis, you need to create a hypothesis statement using both dependent and independent variables. A probable hypothesis statement is as follows: If the company can acquire a new customer management system, then every member of the sales team will be able to bring in at least 15 new clients every month. The new customer management system is the independent variable, while 15 new customers are the dependent variable.
To test this hypothesis, you will begin a pilot project, whereby you involve at least a quarter of your sales team members in the experiment. When choosing the participants, include both the performers and under-performers. Give them the new system and observe their performance for a period of 3 to 6 months: keep in mind the various sales periods. Write down the number of clients that every team member brings in at the end of every month. At the end of the testing period, use statistical methods to find out the average number of clients brought in by each sales team member. If they are at least 15 (or more than 15), then your hypothesis is correct. As a result, you need to implement a new system in the organization to improve productivity.
A null hypothesis happens when the average number of clients brought in by every member of the sales team is less than 15. It means that the hypothesis is incorrect, and you need to go back to the drawing board. A null hypothesis means that you need to find a new way to increase worker productivity.
The above hypothesis test study is only meant to act as an example. If you have understood what hypothesis testing is all about, try to create a unique example of a different scenario.