Email discussion lists for the UK Education and Research communities

## EVIDENCE-BASED-HEALTH@JISCMAIL.AC.UK

#### View:

 Message: [ First | Previous | Next | Last ] By Topic: [ First | Previous | Next | Last ] By Author: [ First | Previous | Next | Last ] Font: Proportional Font

#### Options

Subject:

Re: Of p-values and Confidence Intervals

From:

Date:

Mon, 13 Mar 2006 14:00:33 +0000

Content-Type:

text/plain

Parts/Attachments:

 text/plain (48 lines)
 ```Dear Howard Our stats group has just discussed this over lunch. Points made are somewhat similar to those already provided. We disagree with the "comment in another forum" . Usually a confidence interval is computed making the same assumptions about distributional forms as significance tests use in computing a P-value. We are used to seeing 95% confidence intervals computed as a value plus-or-minus 1.96 times the standard error - this is using the same large sample approximations that are used to compute a z-value by dividing a value by its standard error, from which a P-value is computed using the Normal distribution. In some circumstances P-values are computed using a slightly different large sample approximation than that used in the confidence interval computation. For the risk difference situation you probably use Fisher's test or a chi-squared test for your P-value, and the normal approximation to compute a standard error for computation of the confidence interval. Some might argue that it is the approximations in computing the P-value that are less the devil here, and I would prefer to trust the P-value over the confidence interval, although the difference must be negligible, and only of importance to those who live their lives concerned that P=0.049999 is really very different from P=0.0500001. Most of us think there are more important things in life to worry about. Confidence intervals help us estimate effects, and are very important in telling us how a range of possible results which we should bear in mind. If you use a P-value simply to dichotomise the world into significant or not significant, then we agree that there does not appear to be a reason to include them. But P-values also allow us to directly measure something about the strength of evidence. For example, noting differences between P-values of 0.049 and P-values of 0.00001. This is especially true when you are looking at monitoring clinical trials, where stopping rules are formulated on the strength of evidence. It isn't a one-to-one relationship between width of the confidence interval and P-value, as the size of the effect also has a role. What we do think is outrageous are journals which still round our P-values to P<0.05, rather than let us give the exact values. Also journals who waste space giving the chi-squared or F-value, its degrees of freedom, and the P-value, especially when they tell us that there is no space left for the confidence interval. Jon Deeks Centre for Statistics in Medicine Oxford ```