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After completing this chapter, the reader will be able to

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  • Describe the importance of statistical analysis in completing and evaluating empirical studies.
  • Identify and define the four scales of variable measurement.
  • Define descriptive and inferential statistics.
  • Describe several common epidemiological measures.
  • Describe the properties of several commonly used probability distributions.
  • Identify and describe the difference between parametric and nonparametric statistical tests.
  • Discuss the assumptions of commonly used parametric and nonparametric statistical tests.
  • Determine whether the appropriate statistical test has been performed when evaluating a study.
  • Determine whether the statistical test was interpreted appropriately when evaluating a study.

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  • Image not available. There are four scales of measurement—nominal, ordinal, interval, and ratio.
  • Image not available. The mean is the most appropriate measure of central tendency for normally distributed interval or ratio variables, while the median is the most appropriate measure for ordinal variables or skewed distributions.
  • Image not available. The variance and standard deviation are the appropriate measures of variability for normally distributed variables measured on an interval or ratio scale.
  • Image not available. The central limit theorem states that when equally sized samples are drawn from a non-normal distribution, the plotted mean values for each sample will approximate a normal distribution.
  • Image not available. Clinical significance is far more important than statistical significance.
  • Image not available. Adaptive designs have strengths, but the limitations are substantial. Thus, researchers have to consider the limitations a priori.
  • Image not available. The decision of which statistical test to employ is based on several factors—research question, study design, dependent variable and independent variable considerations, and assumption violations—most of which are interconnected.

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Knowledge of statistics is essential to understanding empirical literature within the biomedical sciences. This chapter will provide a basic understanding, and applied application, of descriptive and inferential statistics for the reader who has little or no statistical background. The focus of this chapter is to describe concepts as they relate to evaluating medical literature, as opposed to discussing the mathematical underpinnings, calculation, and programming of any specific statistical test. This chapter 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 the study.

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Before discussing the types of statistics used in biomedical literature, it may be helpful to review information regarding study design, dependent variable (DV) and independent variable (IV), and the four scales of measurement. The first section of this chapter will discuss basic concepts about populations, samples, data, and variables while the second section will discuss specific descriptive and inferential statistics.

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Populations and Samples

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When investigating a particular research question or hypothesis, researchers must first define the population to be studied. A population refers to all objects of a similar type in the universe, while a sample is a fraction of the population chosen to be representative of the specific population of interest. Thus, samples are chosen to make specific generalizations about ...

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