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• Three factors help determine whether an observed estimate, such as the mean, is different from a norm: the size of the difference, the degree of variability, and the sample size.
• The t distribution is similar to the z distribution, especially as sample sizes exceed 30, and t is generally used in medicine when asking questions about means.
• Confidence intervals are common in the literature; they are used to determine the confidence with which we can assume future estimates (such as the mean) will vary in future studies.
• The logic behind statistical hypothesis tests is somewhat backwards, generally assuming there is no difference and hoping to show that a difference exists.
• Several assumptions are required to use the t distribution for confidence intervals or hypothesis tests.
• Tests of hypothesis are another way to approach statistical inference; a somewhat rigid approach with six steps is recommended.
• Confidence intervals and statistical tests lead to the same conclusions, but confidence intervals actually provide more information and are being increasingly recommended as the best way to present results.
• In hypothesis testing, we err if we conclude there is a difference when none exists (type I, or α, error), as well as when we conclude there is not difference when one does exists (type II, or β, error).
• Power is the complement of a type II, or β, error: it is concluding there is a difference when one does exist. Power depends on several factors, including the sample size. It is truly a key concept in statistics because it is critical that researchers have a large enough sample to detect a difference if one exists.
• The P value first assumes that the null hypothesis is true and then indicates the probability of obtaining a result as or more extreme than the one observed. In more straightforward language, the P value is the probability that the observed result occurred by chance alone.
• The z distribution, sometimes called the z approximation to the binomial, is used to form confidence intervals and test hypotheses about a proportion.
• The width of confidence intervals (CI) depends on the confidence value. 99% CI are wider than 95% CI because 99% CI provide greater confidence.
• Paired, or before-and-after, studies are very useful for detecting changes that might otherwise be obscured by variation within subjects, because each subject is his or her own control.
• Paired studies are analyzed by evaluating the differences themselves. For numerical variables, the paired t test is appropriate.
• The kappa κ statistic is used to compare the agreement between two independent judges or methods when observations are being categorized.
• The McNemar test is the counterpart to the paired t test when observations are nominal instead of numerical.
• The sign test can be used to test medians (instead of means) if the distribution of observations is skewed.
• The Wilcoxon signed rank test is an excellent alternative to the paired t test if the observations are not normally distributed.
• To estimate the needed sample size for ...

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