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

Using the chi-square to illuminate the gray areas:

In the last two examples (42% and 90%), it was pretty obvious what the chi-square test would say. In this last case, where 50% of sick days fall on M/F, it's not so obvious. This is a case where the statistical test can help resolve a gray area. Here goes...

	observed ("o")	expected ("e")	(o-e)	(o-e)2	(o-e)2/e
Monday/ Friday
Midweek
Total	100	100
Check

 

You should have gotten a chi-square-calc of 4.166, compared to the chi-square-crit of 3.84. So, its a close call, but the test says that the engineer's random sickday model probably does NOT hold up. The test can't tell you this for sure, but it still gives you an objective way to say "(probably) yes" or "(probably) no" when you're in a "gray" area.