Conducting hypothesis testing is necessary because in most Six Sigma quality improvement projects, a lot of money is at stake and if unproductive changes are made, it could seriously affect both the company's finances as well as its productivity. To avoid such undesirable situations, businesses need to test the efficacy of the proposed changes with the help of hypothesis testing tools and techniques.



Defining Test Parameters

Hypothesis testing is not something based on gut feelings or is conducted manually. It is conducted with the help of advanced statistical tools because even a small fault in the proposed change can have severe implications on the business process where it is being implemented. Advanced statistical tools however, do not guarantee accurate results because a lot more depends on the defined objectives of the hypothesis testing and the various variables that need to be considered.

If there are faults in the defined objectives and other testing parameters, it will become quite difficult to ensure the success of the proposed changes even if the test results are positive. Defining the correct testing parameters should therefore be the primary aim of Six Sigma project implementation teams.

Authenticating Data

Defining the correct parameters is certainly necessary, but it is not the only thing that needs to be done. After the parameters have been defined, the next task is to classify the collected data and also ensure that it is free from errors and inaccuracies. This is necessary because the test results will not reflect the real picture in case there are errors in the data itself. Only after all the above stated factors have been taken into consideration can the project team hope to ensure the effectiveness of the hypothesis testing initiatives.

Limitations

Hypothesis testing certainly helps in testing the effectiveness of proposed changes, but it is not something that can be used to certify the success of proposed process changes. This is because there are many other variable factors that can affect a business process such as employee dissatisfaction or changes in technology, all of which cannot be predicted and certainly cannot be tested with the help of hypothesis testing.

All the analyst can do is to make adequate provisions for all these factors and then compare test results to find and select a proposed change that confirms to the suggested hypothesis. However, this type of hypothesis testing is not foolproof because it is very difficult to the determine the exact affects that a proposed change will have in the face of unforeseen events such as falling employee morale or technology changes.

A company implementing Six Sigma for the first time may not have adequate talent or resources for conducting hypothesis testing. To make the best use of this tool, such companies can hire experts from outside or provide adequate training to existing employees. All this may cost the company a lot of money, but when we consider the stakes involved, the costs seem quite insignificant. Hypothesis testing is a necessity and should be conducted even if it costs top dollar.