Statistical significance Clinical significance Essay Assignment Paper
Statistical significance Clinical significance Essay Assignment Paper
Please do a paragraph about this post with this instruction .
post most have 4 or more sentences .
you also have to have a high quality post from a content perspective. This means it also needs to do more than agree with or praise a class mate. If you agree with a classmate, explain why, give an example, share what you learned in the readings, ask questions of each other, etc
Statistical significance considers the first and third of these concerns. The middle one, bias, cannot be detected by mathematical deductive logic: it needs detailed information on the way the sample was chosen. This is dealt with in the notes on bias. Consider a study that shows a new therapy to be superior to the existing therapy. Statistical significance calculates the probability that the results observed in a study may have been merely a chance finding, and would not be repeated if the study were re-done. It depends on the sample size (the bigger the sample, the more confident you will be that it produces trustworthy results) and the size of the difference observed. If the study showed a huge difference between new and old therapies, the result is more likely to be real. Crucial Point: testing statistical significance is all about the likelihood of a chance finding that will not hold up in future replications. Significance does not tell us directly how big the difference was. Clinical significance or clinical importance: Is the difference between new and old therapy found in the study large enough to alter a practice. Because there is always a leap of faith in applying the results of a study to patients, a small improvement in the new therapy is not sufficient to cause to alter the clinical approaches. Note that it would almost certainly not alter an approach if the study results were not statistically significant. But the difference between two therapies large enough to alter the practice is if statistics cannot fully answer this question. It is one of clinical judgment, considering the magnitude of benefit of each treatment, the respective profiles of side effects of the two treatments, their relative costs, the comfort with prescribing a new therapy, the patient’s preferences. But we can provide different ways of illustrating the benefit of treatments, in terms of the Number Needed to Treat. To decide whether a new treatment should be used, statistical significance of its effectiveness over current treatment alone is insufficient. Measures of the size of the treatment effects (that is, clinical significance) are also necessary. Statistical significance measures how likely that any apparent differences in outcome between treatment and control groups are real and not due to chance. Clinical significance measures how large the differences in treatment effects are in clinical practice. Different measures have been devised. A partial way out of this uncertainty is to express study results using confidence intervals instead of significance levels. Confidence intervals show the likely range of results within which the true value is likely to lie. An important idea to grasp is that if a study is very large, its result may be statistically significant unlikely to be due to chance, and yet the deviation from the null hypothesis may be too small to be of any clinical interest. Conversely, the result may not be statistically significant because the study was so small or “under powered”, but the difference is large and would seem potentially important from a clinical point of view.
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