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The Many Variables & The Spurious Waffles
Published in Richard McElreath, Statistical Rethinking, 2020
The resulting plot appears in Figure 5.5. It’s easy to see from this arrangement of the simulations that the model under-predicts for States with very high divorce rates while it over-predicts for States with very low divorce rates. That’s normal. This is what regression does—it is skeptical of extreme values, so it expects regression towards the mean. But beyond this general regression to the mean, some States are very frustrating to the model, lying very far from the diagonal. I’ve labeled some points like this, including Idaho (ID) and Utah (UT), both of which have much lower divorce rates than the model expects them to have. The easiest way to label a few select points is to use identify:
Instrument evaluation
Published in C M Langton, C F Njeh, The Physical Measurement of Bone, 2016
Christopher F Njeh, Didier Hans
In interpreting data due to treatment effect, clinicians should be aware of the statistical phenomenon known as regression to the mean. Regression towards the mean occurs whenever we select an extreme group based on one variable and then measure another variable for that group. The second group mean will be closer to the mean for all subjects than the first, and the weaker the correlation between the two variables the bigger the effect will be [10]. This phenomenon is due in part to measurement error and biological variation [11]. The clinical consequence is that a subject that has demonstrated a particularly high BMD gain in the first year after treatment is likely to show a reduced increase or perhaps even a decrease in BMD in the second year. Similarly, a subject who has lost an unexpectedly large amount of bone after the first year of treatment is likely to show less of a loss, even a gain, in the second year. Over a longer period of time, the extremes will be less deviant from the mean because some of the errors of repeated measurements will smooth the results [12]. Cummings et al [13] demonstrated this effect using data from two randomized, double-blinded, placebo-control trials of alendronate and raloxifene treatment. They concluded that effective treatment for osteoporosis should not be changed because of loss of BMD during the first year of use.
Judging the Scientific Quality of Applied Lighting Research
Published in LEUKOS, 2019
Jennifer A. Veitch, Steve A. Fotios, Kevin W. Houser
Internal validity concerns the confidence that the reader can have that the conclusions drawn about a particular cause-and-effect relationship were warranted; colloquially, it concerns the judgment of how well the investigation was performed and reported. There are several important threats to internal validity (Cook and Campbell 1979; Shadish et al. 2002), but the most important considerations for lighting research are the following: Confounding: The failure to exclude plausible alternative explanations.Selection bias: Where groups are not equivalent at the start of the investigation.Regression toward the mean: In which extreme scores tend toward the mean over repeated testing.Testing effects: Changes in the outcome measurements as a result of repeated measurement.