Agreed with regards to the small difference....I'm just trying to find some significance in the difference so I don't have to tell people the bad news about our promotion!

However, I did run the t-test with a redemption as a 1 and a non-redemption as a 0. Took the average of that field and compared those means.....Surprise surprise, came out with the same p-value as I did with the chi square...

Thanks for the help,

Larry

-----Original Message----- From: Peter Flom [mailto:flom@ndri.org] Sent: Monday, April 01, 2002 2:49 PM To: SAS-L@LISTSERV.UGA.EDU; LKaskey@TRACFONE.COM Subject: Re: Chi Square interpretation

You would probably want to transform the proportion using, .e.g. the arcsine transformation or the logit.

I spoke too quickly about t-test, which, you are correct, wouldn't be right with raw proportions.

But, as I noted, the bigger issue is what the data mean, not whether they are significant.

6.9% vs. 6.4% is, for most applications, a very small difference. Whether this is so for you is something you would know.

Peter

>>> Larry Kaskey <LKaskey@TRACFONE.COM> 04/01/02 01:54PM >>> Thanks Peter, but I thought, perhaps wrongly, that a t-test would work for continuous data, such as comparing the time it takes for a person to redeem the coupon, where a chi square would be better for catagorical data such as in my case, response or no response.

My statistics is very rusty so I am easily influenced!

Thanks for the help,

Larry Kaskey Retention Analyst 305-418-2412

-----Original Message----- From: Peter Flom [mailto:flom@NDRI.ORG] Sent: Monday, April 01, 2002 1:43 PM To: SAS-L@LISTSERV.UGA.EDU Subject: Re: Chi Square interpretation

One question is why do Chi-sqare rather than a t-test?

You have two samples, and a proportion from each who did something.

And you can then get a confidence interval around the difference.

However, on the data you gave, it seems to me like your conclusion is correct. It just might be a type 3 error - solving the wrong problem

The other big question is, whether or not any test is significant or not, you care, on a substantive level, about the difference in the two rates. Even if 6.9% and 6.4% are exactly the right values, would you want to go ahead?

HTH

Peter L. Flom, PhD Assistant Director, Statistics and Data Analysis Core Center for Drug Use and HIV Research National Development and Research Institutes 71 W. 23rd St New York, NY 10010 (212) 845-4485 (voice) (917) 438-0894 (fax)