Hypothesis testing and p values: how to interpret results and reach the right conclusions.
نویسندگان
چکیده
Whenever we encounter a research finding based on the interpretation of a p value from a statistical test, whether we realise it or not, we are discussing the result of a formal hypothesis test. This is true irrespective of whether the test involves comparisons of means, Odds Ratios (ORs), regression results or other types of statistical tests. As readers of research, it is important to understand the underlying principles of hypothesis testing, so that when faced with statistical results, we reach the right conclusions and make good decisions about which findings are robust enough to be translated into clinical practice. The article by Yinon et al featured in a recent EBN commentary, will be used to illustrate four simple steps involved in hypothesis testing. The authors of this paper explored the possible benefits of antenatal steroid administration in the context of late preterm birth (>34 weeks gestation). One of the key outcomes of interest included the incidence of babies being admitted to a special care unit (SCU). It was hypothesised that steroid administration would lead to better respiratory function and therefore reduction in SCU admissions. In the sample, 14 of 83 neonates (almost 17%) in the experimental (steroid) group were admitted to SCU, compared with 24 of 84 neonates (almost 29%) in the control (no steroids) group. At first glance we see a difference in the two groups, however, we need to look further and decide whether the differences found represent real differences in SCU admission rates due to antenatal steroid administration. It may be plausible that the differences observed are due to random differences within the sample studied. Let’s follow four simple steps to reach a conclusion about these results.
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ورودعنوان ژورنال:
- Evidence-based nursing
دوره 16 2 شماره
صفحات -
تاریخ انتشار 2013