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Learning Objectives

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After completing this chapter, the reader will be able to

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  • Define the population being studied and describe the method most appropriate to sample a given population.

  • Identify and describe the dependent and independent variables and indicate whether any covariates were included in analysis.

  • Identify and define the four scales of variable measurement.

  • Describe the difference between descriptive and inferential statistics.

  • Describe the mean, median, variance, and standard deviation and why they are important to statistical analysis.

  • Describe the properties of the normal distribution and when an alternative distribution should be, or should have been, used.

  • Describe several common epidemiological statistics.

  • Identify and describe the difference between parametric and nonparametric statistical tests and when their use is most appropriate.

  • Determine whether the appropriate statistical test has been performed when evaluating a study.

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Key Concepts

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  1. There are four scales of variable measurement consisting of nominal, ordinal, interval, and ratio scales that are critically important to consider when determining the appropriateness of a statistical test.

  2. Measures of central tendency are useful to quantify the distribution of a variable’s data numerically. The most common measures of central tendency are the mean, median, and mode, with the most appropriate measure of central tendency dictated by the variable’s scale of measurement.

  3. Variance is a key element inherent in all statistical analyses, but standard deviation is presented more often. Variance and standard deviation are related mathematically.

  4. The key benefit to using the standard normal distribution is that converting the original data to z-scores allows researchers to compare different variables regardless of the original scale.

  5. The last observation carried forward (LOCF) technique used often with the data from clinical trials introduces significant bias into the results of statistical tests.

  6. The central limit theorem states when equally sized samples are drawn from a non-normal distribution, the plotted mean values from each sample will approximate a normal distribution as long as the non-normality was not due to outliers.

  7. There are numerous misconceptions about p values and it is important to know how to interpret them correctly.

  8. Clinical significance is far more important than statistical significance. Clinical significance can be quantified by using various measures of effect size.

  9. The selection of the appropriate statistical test is based on several factors including the specific research question, the measurement scale of the dependent variable (DV), distributional assumptions, the number of DV measurements as well as the number and measurement scale of independent variables (IVs) and covariates, among others.

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Introduction

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Knowledge of statistics and statistical analyses is essential to constructively evaluate literature in the biomedical sciences. This chapter provides a general overview of both descriptive and inferential statistics that will enhance the ability of the student or evidence-based practitioner to interpret results of empirical literature within the biomedical sciences by evaluating the appropriateness of statistical tests employed, the conclusions drawn by the authors, and the overall quality of ...

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