Comments on: Hypothesis Testing: The Basics

By: fdfgdf@ggg.com

fdfgdf@ggg.com — Thu, 03 Jun 2010 13:53:10 +0000

very stupid

By: charix

charix — Thu, 14 Jan 2010 22:11:46 +0000

tnx..

By: Bill Smith

Bill Smith — Mon, 07 Dec 2009 17:08:04 +0000

Jesse, thank you for writing this series of articles on statistics.

I understand that small z-scores are better than big ones, but where is the dividing line?

By: Traveller_Adventure

Traveller_Adventure — Sun, 02 Aug 2009 06:01:31 +0000

What a useful post here. Very informative for me..TQ friends...

Cheers,
Blog Review

By: Jesse Farmer

Jesse Farmer — Thu, 04 Jun 2009 20:29:04 +0000

The answer to what?

By: kayla

kayla — Thu, 04 Jun 2009 20:26:17 +0000

i would like for this page to tell me the answer not give me examples. i need an answer not stupid examples.please thank you

By: Jesse Farmer

Jesse Farmer — Wed, 22 Apr 2009 23:37:06 +0000

You're right, I equivocated in my use of “p”. At first I use it to mean the value of p under the null hypothesis, H_0: p = 0.50, but then use it to mean the measured value elsewhere.

I'll fix it.

By: Charles

Charles — Wed, 22 Apr 2009 23:17:20 +0000

I think that the "p" in the denominator for the "z score" should be the hypothesized value, in this case 0.50. You table uses calculated values to find z.

This text is from the referenced web site. (McClave and Sincich also define the formula that way.)
"Analyze Sample Data
Using sample data, find the test statistic and its associated P-Value.
• Standard deviation. Compute the standard deviation (σ) of the sampling distribution.
σ = sqrt[ P * ( 1 - P ) / n ]
where P is the hypothesized value of population proportion in the null hypothesis, and n is the sample size."

By: Cindy P Dennis

Cindy P Dennis — Wed, 04 Feb 2009 06:38:04 +0000

Excellent, entertaining, useful reading, Thanks !!

By: Jesse Farmer

Jesse Farmer — Thu, 20 Nov 2008 21:46:27 +0000

Let me put my question another way.

You have a coin and don't know if it's fair. You flip it 100 times and it lands on heads 51 times.

What can you say about the coin? Can you say it's fair? Can you say it's biased?

More generally, it's difficult, if not impossible, to prove a hypothesis is correct. You can prove a hypothesis is false, however. So if you want to know whether a coin is biased you should see whether the data falsifies the converse, viz., that the coin is biased.