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Confidence intervals focus our attention on population values, which is what theory is all about. They highlight our degree of uncertainty. If the confidence interval includes the null hypothesis value, we know that we do not have a statistically significant result. In this way confidence intervals provide similar information as NHST, but also much more. Power Analysis: Having an adequate sample size to assess your research question is important. Statistical power is the probability of finding a statistically significant result for a particular parameter in a particular study where the null hypothesis is false. Power increases with larger population effect sizes, larger sample sizes and less stringent alpha. G-Power 3 (just Google G Power 3) is excellent free software for running power analyses. Accuracy in Parameter Estimation (AIPE): Power analysis is aligned with NHST. AIPE is aligned with confidence intervals around effect sizes and meta-analytic thinking. AIPE attempts to work out the size of the confidence interval we will have for any given sample size and effect size.

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