Andersons pediatric cardiology 631

population The larger your sample, the more precise the measurement and the closer you will be to the true mean This is because based on the actual distribution of blood pressures in the population, more individuals have a value near 100 mm Hg, and with increased samples, each individual value contributes less to the total, so extreme values have less effect on the mean How do we tell whether measurements are different from each other by chance or truly different? Consider this situation: a researcher polls a random sample of 100 pediatric cardiologists regarding their preferred initial therapy for heart failure, finding that 72% of the sampled physicians prefer using ACE inhibitors (ACEIs) over β-blockers Since the sample was chosen at random, the researcher decides that it is a reasonable assumption that this group is representative of all pediatric cardiologists A report is published titled, “ACEIs Are Preferred Over β-Blockers for the Treatment of Heart Failure in Children.” Had all pediatric cardiologists been polled, would 72% of them have chosen ACEIs? If another researcher had selected a second random sample of 100 pediatric cardiologists, would 72% of them also have chosen ACEIs over βblockers? The answer in both cases is probably not, but if both the samples came from the same population and were chosen randomly, the results should be close Next, suppose that a new study is subsequently published reporting that βblockers are actually better at improving ventricular function than ACEIs You subsequently poll a new sample of 100 pediatric cardiologists and find that only 44% now prefer ACEIs Is the difference between your original sample and your new sample due to random error, or did the publication of the new study have an effect on preference in regard to therapy for children in heart failure? The key to answering this question is to estimate the probability by chance alone of obtaining a sample in which 44% of respondents prefer ACEIs when, in fact, 72% of the population from which the sample is drawn actually prefer ACEIs In such a situation, inferential statistics can be used to assess the difference between the distribution in the sample as opposed to the population, and the likelihood or probability that the difference is due to chance or random error Relationship Between Probability and Inference Statistical testing comparing two groups starts with the hypothesis that both groups are equivalent, also called the null hypothesis A two-tailed test tests the probability that group A is different than group B, either higher or lower, whereas a one-tailed test tests the probability that group A is either specifically higher or lower than group B but not both Two-tailed tests are generally used in medical research statistics (with a common exception being noninferiority trials) Statistical significance is reached when the P value obtained from the tests is under 0.05, meaning that the probability that both groups are equivalent is lower than 5% The P value is an expression of the confidence we might have that the findings are true and not the result of random error Using our previous example of preferred treatment for heart failure, suppose the P value was