Timeline for interpreting my own data (negative confidence interval in epidemiology)
Current License: CC BY-SA 3.0
11 events
| when toggle format | what | by | license | comment | |
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| Jul 27, 2014 at 18:11 | comment | added | shadowtalker | For general stats, I've heard good things about Statistical Models by Freedman, Statistics for Experimenters by Box, Statistics in a Nutshell published by O'Reilly, Statistics in Plain English by Urdan. | |
| Jul 27, 2014 at 17:36 | comment | added | shadowtalker | Well, the stuff about "negative decrease is an increase" is just math. As far as hypothesis testing goes, I first learned statistics through an econometrics lens, from Introduction to Econometrics by Stock & Watson, and then later from A Second Course in Statistics: Regression Analysis by Mendenhall & Sincich. They both have a good deal about hypothesis tests. There might also be some good references here: stats.stackexchange.com/questions/31800/… | |
| Jul 27, 2014 at 12:52 | comment | added | Heala45 | Thank you. Truly instructive. Do you know any book on this specific subject, or does most books in statistic cover this topic? | |
| Jul 27, 2014 at 11:43 | vote | accept | Heala45 | ||
| Jul 27, 2014 at 11:43 | |||||
| Jul 26, 2014 at 18:46 | comment | added | shadowtalker | And you've uncovered the problem with frequentist statistics: assuming the existence of a single true underlying parameter value tends to be conceptually problematic. This is why people have come to interpret "failure to reject exact equality" as "close enough to equality." All you're doing in the latter case is letting your textbook choose a formula for "close enough" based on sample size and some distributional assumptions. Sometimes that's what you want, other times it isn't. | |
| Jul 26, 2014 at 18:42 | comment | added | shadowtalker | Yes, having values on the same side of 0 is equivalent to having a test statistic above the rejection cutoff. I recommend you sit down with a pen and paper and prove this for yourself, it's a great exercise that really forces you to understand how hypothesis testing and CIs work. | |
| Jul 26, 2014 at 18:41 | comment | added | shadowtalker | You have not proved that they are equal. You've only failed to rule out the possibility that the means are equal, up to a 1 in 20 chance of having missed a difference. | |
| Jul 26, 2014 at 17:41 | comment | added | Heala45 | Okey. Intuitively It feels that the absolute change is significant. Can it be that whenever an effect is significant, all values in the confidence interval will be on the same side of zero (either all positive or all negative). Can I therefore specify the direction of the effect. There are many situations in which it is very unlikely two conditions will have exactly the same population means. Therefore, even before an experiment is conducted, I know beforehand that the null hypothesis of exactly no difference is false. I might be wrong, if so please be sure to correct me. | |
| Jul 26, 2014 at 16:11 | comment | added | shadowtalker | Yes. You're stuck in the terminology. A negative decline is an increase. The lower bound on your decline is an upper bound on your change, i.e. your increase. This is why it's generally easier to think in positive units, it can be confusing. BTW, your CI for change includes 0 so you can't reject the null hypothesis of zero change with 95% confidence. | |
| Jul 26, 2014 at 14:43 | comment | added | Heala45 | So your saying that a confidence interval for the lower limit below zero is an actual increase? For all i know u might be fully correct, cant just understand it quite yet. Since most of my variables have an absolute change very close to zero my lower limits are all negative. How can it then be that both the lower limit and upper is greater then the point estimate. For example i just did BMI (body mass index), it showed an absolute decline of 8,6 (-11,2 to 44,1). How would u interpret this? Btw really appreciating ur help! | |
| Jul 26, 2014 at 14:33 | history | answered | shadowtalker | CC BY-SA 3.0 |