Evidence-based Clinical Practice – Full course summary (UNIVERSITY OF AMSTERDAM)
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There appears to be a high rate of false positives in science. The p-value refers to the conditional probability that the present data or more extreme will be observed in a random sample given that the null hypothesis is true. Critique of using the p-value includes not interpreting the p-value properly (1) and the fact that the p-value varies with a varying sample size (2).
The effect size refers to the magnitude of the found statistical relationship. Unstandardized effect sizes are preferred when there is a clear consensus regarding that the measurement unit is in the interval level (e.g. seconds; blood pressure).When this is not the case (e.g. in psychology), standardized effect sizes should be used. The effect size can be easily interpreted if the sample sizes are equal and the total sample size is moderate to large. However, this becomes more complex if the sample sizes differ greatly.
Power refers to the probability that a true effect of a precisely specified size in the population will be detected using significance testing. It is the probability of finding an effect given that an effect of the specified size (i.e. the power) exists. The statistical power is one minus the type II error rate. The type II error rate refers to the probability that a true effect will not be detected using significance testing. It is the probability that the alternative hypothesis is wrongfully rejected.
Power should be maximized in a study. The power, however, is affected by the sample size (1), the measurement error (2) and the homogeneity of the participants (3). According to Cohen, research should use power of at least 0.8.
It is problematic to solely focus on the observed p-value because findings with equivalent p-values can have very different implications. In cases with large sample sizes, very small effect sizes can be significant and in cases with small sample sizes, a significant effect may produce an implausible large effect size. Therefore, there is a big difference between practical and statistical significance. A lot of research with small sample sizes is underpowered.
There are several recommendations to improve research:
Conducting multiple tests of significance on a dataset without statistical correction (1), running participants until significant results are obtained (2), dropping observations, measures, items, conditions or participants after looking at the effects on the outcomes of interest (3) and running multiple experiments with similar procedures and only reporting those with significant results (4) are questionable research practices.
There are several recommendations for educational practice:
Replication studies might need to use a lower than reported effect size (1), develop a minimum value for an effect size that is deemed to small (2) and use a higher value than 0.80 for statistical power (3). This makes sure that replication is possible because if this is not done, the probability of finding a significant result decreases steadily. These steps enhance the credibility and the usefulness of a replication study.
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This bundle gives a full overview of the course "Evidence-based Clinical Practice" given at the University of Amsterdam. It contains both the articles and the lectures. The following is included:
This bundle contains an overview of all the articles used in the course "Evidence-based Clinical Practice." given at the University of Amsterdam. It contains the following articles:
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