Errors in Hypothesis Testing

The Possibility of Mistake

Hypothesis testing is all about statistical analysis. It depends upon the data that was collected at the measure stage. It also depends upon the fact that the critical few inputs were possibly identified earlier. There is still an element of chance. There is still a possibility that a mistake could be made and that the conclusion reached after hypothesis testing may be invalid.

The hypothesis testing method takes this into account. The Six Sigma project person therefore have to choose between the two possible error types that could be made. It is basically a trade-off, if you try to protect yourself from one error, you expose yourself to the other. In some situations it is better to be exposed to one error than to the other. Here is the explanation:

The Two Types of Errors:

  1. Alpha Error

    Alpha error is the risk of stating that there is a particular input which is a part of the vital few inputs when in reality it is not. An example would be stating that location of Branch A makes its service better than branch B, when in reality location may not be an important factor determining service at all.

    How can this Error be Avoided ?

    This error can be avoided by increasing the confidence level for the hypothesis. But if this is done there is a risk of Beta error i.e. missing the vital input

  2. Beta Error

    Beta error is the risk of stating that there is a particular input which is not a part of the vital few inputs when in reality it is. An example would be the failure to mention location as an important factor of the service being rendered at Branch A, when in reality the location may be the cause of the excellent service.

    How can this Error be Avoided ?

    This error can be avoided by decreasing the confidence level for the hypothesis. But if this is done there is a risk of Alpha error i.e. wrongly considering an input to be a vital input

Choosing which error to make

Both Alpha and Beta errors are related to one another. Trying to avoid one error too much leads to the other error. Hence Six Sigma practitioners must understand the cost of making both the mistakes and choose the mistake they would rather make. Since there is no way to be perfect, we will find a way to be least erroneous.


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