This paper is also open access.
This paper is also open access.
One of these days we’ll progress from talking about neurotransmitters and start talking about neurotransmitter receptors. Ah, one can dream :-).
Also, I’m back. I’ll get on answering Q’s and filling up the queue soon :-)
Chronic starvation is accompanied by a reduction in resting energy expenditure (REE). It is not clear whether this is due mainly to a reduction in body mass or also involves a significant reduction in the cellular metabolic rate of the fat-free mass (FFM).
- Measured REE was significantly lower than predicted REE in subjects with AN, but not in control subjects.
- In addition, REE adjusted for both FFM and fat mass was significantly lower in the subjects with AN.
- Finally, compared with the lean control subjects, both organ and skeletal muscle mass were approximately 20% smaller in subjects with AN.
Chronic starvation is accompanied by a significant reduction in the metabolic rate of the FFM. The organs and/or tissues accounting for this are unknown. In addition, this study suggests that protein is mobilized proportionately from organs and skeletal muscle during starvation. This too may be an adaptive response to chronic starvation.
I can! First we have to understand two concepts:
- Null hypothesis - suggests there is no relationship or effect between the independent and dependent variable. So if we are testing an antidepressant, the null hypothesis suggests the medication has no effect on depression levels.
- Alternative hypothesis - suggests there is a relationship or effect between the independent and dependent variable. So for that antidepressant, this might suggest the medication has some effect on depression levels.
Statistical significance in its proper label is called null hypothesis significance testing. When testing for statistical significance between our independent and dependent variables, we follow a specific process:
- Assume the null hypothesis is true and that there is no effect (fun fact: this is never the case, but we assume it anyway).
- We apply a statistical model to our data in a way that represents the alternative hypothesis (i.e., that there is some effect) and see how strong of a fit it is while still assuming the null is true.
- We then calculate the probability of this new model “fitting” when we assume that the null hypothesis is true (i.e., “if there truly was no effect, then what is the probability of getting the results we see here?”)
- If that probability is sufficiently small (often when p < .05), then we assume that the alternative hypothesis’ model fits the data well and we can reject the null.
Ultimately, statistical significance suggests that the observed results are very likely to be inconsistent with the null hypothesis.
A lot of people think that our p value represents the probability that our results are due to chance, or that statistical significance means we can be “95% confident our results are accurate and not due to chance.” These are not exactly correct. Instead, it’s just the probability that our attained results would be very extreme or unlikely should the null hypothesis be true, and because of the low likelihood, we can probably conclude that there is some effect.
What’s important to remember is statistical significance in no way hints at the importance or size of the effect being observed. Some recent article quoted a researcher as calling it “statistically discernible,” and I think that’s a fantastic term. Something can be immensely significant but be completely meaningless, while something can be very insignificant but be incredibly meaningful. Statistical significance plays virtually no role in making that call.
I’ll be away for the next few days without internets, so if I haven’t responded to your ask yet, it may take a while :/.