Timeline for How to calculate confidence intervals for ratios?
Current License: CC BY-SA 3.0
8 events
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| Mar 2, 2017 at 9:54 | comment | added | PM. | @RoseHartman +1 for the answer. It's just a minor style preference. Having the results with the answer often provides more intermediate detail and saves much scrolling up & down the page. But, point taken, the bottom line results are there in the original question. | |
| Mar 2, 2017 at 9:01 | comment | added | Rose Hartman | @scitamehtam I wrote my answer before koalo provided the example data and clarified that the sample size would be 10 or fewer observations. koalo has since updated the original question to include worked examples from each answer method with the n=5 data, very helpfully. | |
| Mar 2, 2017 at 8:57 | comment | added | PM. | @RoseHartman That's a nice clear description but it would also be nice to see your method applied to the sample of data (n=5) in the question. | |
| Feb 23, 2017 at 17:14 | comment | added | koalo | The histogram above comes from the python script to illustrate my problem. I am not able to get that many measurements from the real-world experiment. At least not for every combination of parameters. I agree that 3 might be too small and maybe about 10 will be possible in the final evaluation, but certainly not much more. So what should I do about that to demonstrate that I was not just lucky to get a single measurement, but that repeating the experiment does not give completely different results? | |
| Feb 23, 2017 at 15:34 | comment | added | Rose Hartman | For just three observations, you should expect a very large confidence interval. From your histogram, it looks like you have many more than three observations --- I assumed your example with 0.99,0.94,0.94 was just to illustrate. If your actual sample size is three, I don't recommend calculating confidence intervals at all (or means, for that matter). | |
| Feb 23, 2017 at 8:20 | comment | added | koalo | That sounds like a good approach, however the results are not what I would expect intuitively: The data_logits for 0.99,0.94,0.94 is 4.59,2.75,2,75, giving a confidence interval of [-2.73,9.47]. Transforming this back gives [0.061,0.999] - much larger than I would expect. | |
| Feb 23, 2017 at 2:32 | history | edited | Rose Hartman | CC BY-SA 3.0 |
added code and output to illustrate
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| Feb 23, 2017 at 0:33 | history | answered | Rose Hartman | CC BY-SA 3.0 |