Uses of reliability: monitoring change in an individual…
{"Image DocumentName": "Uses of reliability: monitoring change in an individual\u2026", "Image ImageDescription": "Think about \u00b1 the typical error as the noise or uncertainty in the change you have just measured. You want to be confident about measuring the signal (smallest worthwhile change), say 0.5%. Example: you observe a change of 1%, and the typical error is 2%. So your uncertainty in the change is 1 \u00b1 2%, or -1% to 3%. So the change could be harmful through quite beneficial. So you can\u2019t be confident about the observed beneficial change. But if you observe a change of 1%, and the typical error is only 0.5%, your uncertainty in the change is 1 \u00b1 0.5%, or 0.5% to 1.5%. So you can be reasonably confident you have a small but worthwhile change. Conclusion: ideally, you want typical error < smallest change. If typical error > smallest change, try to find a better test. Or repeat the test several times and average the scores to reduce the noise. (Four tests halves the noise.)", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}