Example the following table shows both p values and

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Example: The following table shows both p values and confidence intervals for each trial. Study Three Year Survival Interferon No Interferon p-value 95% CI for difference Mandelli 0.75 0.56 0.04 0.02 to 0.38 Peest 0.61 0.71 0.31 -0.27 to 0.07 Westin 0.61 0.55 0.50 -0.11 to 0.23 Table shows the comparison of Survival Rates for Patients with Multiple Myeloma in Remission Treated With or Without Interferon. We see that the p values for three studies are very different. Peest and Westin interpreted their results as showing that interferon did not improve survival because in their studies p > .05. However, Mandelli’s result of p = .04 led him to recommend interferon treatment for all patients with multiple myeloma in remission [Ware JH] This interval defines the range of values that are consistent with the difference measured [Rothman KJ]. Values outside this range would be expected to give study results less than 5% of the time. Thus, if we measure a 10% difference between groups, the true difference could be somewhat higher or lower. If the 95% confidence interval is 5% to 15%, there is 95% likelihood that the observed result was generated by a true treatment difference of 5% to 15%. Discuss the effect sample size has on p-values and confidence intervals and how this affects interpretation of statistical results. Important information we can take from the confidence interval is whether it includes the zero or not. The zero effect size means that there is no effect by the factor. With the effect size and the
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