Table of Contents

• 1: Do those shoes fit?
• 2: The 3-day work week
• 3: The day is saved ... or not
• 4: What you observe vs. expect
• 5: The Chi-Square Equation
• 6: Give it a try
• Detour
• 7: What's a p-value?
• 8: What's a Lookup Table?
• 9: What's a "df"?
• Completing the Chi-Square Test
• 10: Interpreting a chi-square
• 11: The answer, finally
• 12: Summary
• 13: What about 90% M/F sickdays?
• 14: What about 50% M/F sickdays?
• Chi-Square in Biology
• 15: Testing for a dihybrid ratio
• 16: Review and Words of Wisdom

The day is saved ... or not

Apparently we have saved the day ... 40% of sick days SHOULD fall on Monday or Friday, which means that employees are not abusing the system.

 

But wait. What if next year, Evil Pointy-Haired Boss (EPHB) finds that 42% sick days fell on Monday or Friday??? Proof positive, in his view, that employees are out to get him.

Let's be the engineer for a minute. How could we confirm or disprove Evil Pointy-Haired Boss (EPHB's) claim? Clearly 42% is more than 40% -- but how much is too much? Do the extra 2% just represent the natural "slop" around 40%?

Or, what if next year 90% of sickdays fell on Monday or Friday? Would that make you think that the engineer was wrong, and sick-days were not random? What about 50% of sickdays on M/F?

When you do statistics, you are doing two things: first, putting numbers on common sense, and secondly, using a method that allows you to decide on the gray areas. So, what we expect out of statistics is the following:

• if 40.1% of sickdays are M/F: statistics tells me this fits the random sickday model
• if 50% of sickdays are M/F: statistics allows me to make a decision about this "gray" area
• if 90% of sickdays are M/F: statistics tells me this does not fit the random sickday model