
Selecting the correct hypothesis test can be challenging especially in the learning stages. A Six Sigma project manager should understand the formulas and computations within the most commonly applied tests.
In hypothesis testing, samples represent a subset of the population which are used to infer conclusions about the population. There's always a chance or risk (known as alpharisk and betarisk) that the selected sample is not representative of the population and one could infer the incorrect conclusion. Assumptions are inferred that allows the estimation of the probability (known as pvalue) of getting a wrong conclusion.
Statistical software has simplified the work to the point where comprehension of these tests is convenient to overlook.
CAUTION: A statistical difference doesn't always imply a practical difference, numbers don't always reflect reality.
Parametric Tests are used when:
Nonparametric tests are used when:
In general, the power of standard parametric tests are greater than the power of the alternative nonparametric test. As the sample size increases and becomes very large the power of the nonparametric test approaches its parametric alternative.
Nonparametric tests also assume that the underlying distributions are symmetric but not necessarily normal. When the choice exist on whether to use the parametric or nonparametric, if the distribution is fairly symmetric, the standard parametric tests are better choices than the nonparametric alternatives.
For 1 sample: Use Chisquare
For 2 samples: Use the FTest or ANOVA for >2 variances. The Ftest assumes the data is normal.
Levene's test is an option to compare variances of nonparametric data.
For >2 samples: Use Bartlett's Test for parametric data and Levene's Test for nonparametric data
If pvalue < α, reject Ho and accept H_{A}
If pvalue > α, fail to reject the Null, H_{O}
Try to rerun the test (if practical) to further confirm results. The next step is to take the statistical results and translate it to a practical solution.
It is also possible to determine the critical value of the test and use to calculated test statistic to determine the results. Either way, using the pvalue approach or critical value should provide the same result.
The statistical power is 1 minus the betarisk chosen. Usually the betarisk is between 1020% so Power typically range from 8090%.
This is the likelihood of finding an effect when there is actually an effect. This is the chance of rejecting the null hypothesis when the null hypothesis is actually false.
A minimum detectable difference, δ, can also be specified. This detectable difference is used to examine a desired difference among:
The minimum detectable difference desired relative to the standard deviation is the sensitivity of the test. It is the size of the difference expressed in standard deviations.
Similar to the Coefficient of Variation in that the mean is expressed as relative magnitude in standard deviations. The numerator itself doesn't provide much information, it is when it (or the δ) are expressed in terms of standard deviations are you able to compare two or more values with more meaning.
To simplify the testing process, break down the process into four small steps.
Create a table similar to the one below and begin by completing the top two quadrants. The bottomleft contains the results from the test and then converting those numbers into meaning is the practical result which belongs in the bottomright quadrant.
Null Hypothesis characteristics:
This is the hypothesis being tested or the claim being tested. The null hypothesis is either "rejected" or "failed to reject". Rejecting the null hypothesis means accepting the alternative hypothesis.
The null hypothesis is valid until it is proven wrong. The burden of truth rest with the alternative hypothesis. This is done by collecting data and using statistics with a specified amount of certainty. The more samples of data usually equates as more evidence and reduces the risk of an improper decision.
The null hypothesis is never accepted, it can be "failed to reject" due to lack of evidence, just as a defendant is not proven guilty due to lack of evidence. The defendant is not necessarily innocent but is determined (based on the evidence) "not guilty".
There is simply not enough evidence and the decision is made that no change exists so the defendant started the trial as not guilty and leaves the trial not guilty.
Alternative Hypothesis characteristics:
The shape of a distribution is normally distributed
H_{o} = Data is Normal
H_{A} = Data is not Normal
There is a relationship between sales of a toy and placing it on the ends of aisles
H_{O}: Slope = 0
H_{A}: Slope does not equal 0
Supplier ABC’s Part # 34565 weight is not the same as Supplier XYZ’s
H_{o}= Mean ABC = Mean XYZ
H_{A} =Mean ABC does not equal the Mean XYZ
People that eat carrots have better eyesight
H_{o} = eating carrots and eyesight are independent
H_{A } = eating carrots and eyesight are dependent
Running more tests allows you to hone in on the differences conclude more information that can lead to more effective improvements. There are ways to improve the accuracy of results such as being more specific with testing.
For examples, testing for specific numerical differences or looking for differences (or lack of) within a gender, a region, an industry, an age group, a religion, an affiliation, or combination of them.
If you detect a change from large group of people from another that is helpful.....but what about more detail. Therefore, if possibly test the data by gender, by age group, by hair color, by religion, by political party affiliation, by region, etc. You will begin to identify more meaningful information and possibly generate new discussion.
This module in provides lessons and more detail about commonly used hypothesis tests. This is often a new area of study for those learning about the Six Sigma methodology and represents a significant challenge on certification exams and in reallife application.
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