Are
you someone who never imagined you’d be using statistics in your work?
Do you feel, at times, like an undercover interloper in the land of
p-values, as you step gingerly to avoid statistical land mines with
long, complex-sounding names?For example, do you feel a slight chill run down your spine when you read:
“For your analysis results to be valid, you should ascertain whether your data satisfy the assumption of homoscedasticity”?
Sometimes it’s best to face your fears head on.
Granted, homoscedasticity is definitely not a word you should say in public with a mouthful of beer and mashed potatoes. But, like a lot of high-falutin’ specialized terminology, it’s actually much simpler than it appears.
Take a look at its Greek roots.
So, homoscedasticity literally means“ having the same scatter.” In terms of your data, that simply translates into having data values that are scattered, or spread out, to about the same extent.
Homoscedasticity: Why the Big Word for this Simple Concept?
Homoscedasticity is a formal requirement for some statistical analyses, including ANOVA, which is used to compare the means of two or more groups. This requirement usually isn’t too critical for ANOVA--the test is generally tough enough (“robust” enough, statisticians like to say) to handle some heteroscedasticity, especially if your samples are all the same size. However, if you want to compare samples of different sizes, you run a much greater risk of obtaining inaccurate results if the data is not homoscedastic.Luckily, Minitab has a lot of easy-to-use tools to evaluate homoscedasticity among groups.[more...]
Do you feel, at times, like an undercover interloper in the land of p-values, as you step gingerly to avoid statistical land mines with long, complex-sounding names? fengshui
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