Six sigma training tips

photo credit: origami_madness

With six sigma training the aim is not to convert Six Sigma practitioners into statistical experts. Instead, it is to give them the knowledge essential to their success in obtaining business results.

So what should you drop based on the target audience you are trying to reach, here are some ideas:

• Hypothesis tests, such as F-tests and t-tests as these deal with statistical significance which does not necessarily correlate with practical significance. Also hypothesis tests are sample size dependent. With a sufficiently large sample size, you can disprove most statistical hypotheses, therefore establishing statistical significance, even though the results are not of practical significance. Conversely, lack of significance might often be due to inadequate sample size, rather than lack of true effects, therefore resulting in inability to establish statistical significance, even though the effect may be of practical significance.
• Statistical interval statements, such as confidence intervals that quantify the statistical uncertainty, are generally more informative. Similarly, de-emphasize the analysis of variance (except as a tool for estimating components of variation) in favour of graphical displays.
• Place less emphasis on R-squared (the percent of variability accounted for by a fitted regression line) as a measure of association because this, unlike the standard deviation of a fitted regression, provides limited information on prediction ability.

Top tips:

• Always emphasise the graphical tools over formal statistical analyses
• Teach the tools and their applications, and omit the underlying theory but not the key assumptions which make the tool applicable or not
• Use real life examples and case-studies
• Use software, e.g. Minitab to demonstrate hands-on implementation
• Tie the training into a project, with the emphasis on using tools which are appropriate to solve the problem, in other words, don’t be overly prescriptive with what tools are required

Avoid:

• Using historical data
• Being vague about the importance of data normality
• Lack of explanation on how to handle non-normal data
• Spend little time on DoE: often a confusing and complex subject