Timeline for Confidence Interval difference in means Interpretation
Current License: CC BY-SA 4.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 9, 2020 at 17:48 | comment | added | Greg Snow | Einstein said something about how things should be explained as simply as possible, but no simpler. Interpreting confidence intervals is one thing that is very hard to find that balance. It is very easy in the pursuit of making things simple enough to understand to make the explanation too simple and therefore potentially misleading. | |
| Apr 9, 2020 at 0:02 | comment | added | Erin Sprünken | But, indeed, I should have written my answer in another form, I see that there's a problem there. | |
| Apr 9, 2020 at 0:00 | comment | added | Erin Sprünken | The idea of confidence intervals is to say P(C_l <= theta <= C_u) >= 1-alpha. In other words, in 1-alpha * 100 % of the cases the confidence interval encloses the true parameter. However, since we draw random samples, there is a probability of getting confidence intervals which do not enclose the true paramter (e.g.: [5, 10] or whatsoever). We do not know how far or close these are, but the probability of getting such sample mean/differences s is there. | |
| Apr 8, 2020 at 23:43 | comment | added | Greg Snow | So you think that if the true difference in the means is around -35 (a value in the interval) that you would still see 5 out of 100 new samples with mean 1 higher than (or at least not statistically significantly lower than) mean 2? You also think that if the true difference in means is -10 (also in the interval, but much closer to 0), that you will only see 5 out of 100 new samples with mean 1 not significantly lower than mean 2? Your interpretation is based on power (and the true mean difference), not the confidence interval. | |
| Apr 8, 2020 at 23:06 | history | answered | Erin Sprünken | CC BY-SA 4.0 |