MathBench presents...

Table of Contents

• 1: Do those shoes fit?
• 2: Dilbert's 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 Dilbert's random sickday model probably does NOT hold up. The test can't tell you this for sure, but it still gives you a way to say "(probably) yes" or "(probably) no" when you're in a "gray" area.