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Identify and describe the two general functions of statistics
Describe measurement and variable classification schemes
Describe and use measures of central tendency and dispersion
Organize and present data in a scientifically meaningful way
Differentiate between proportions and rates, including prevalence and incidence
Describe performance measures for diagnostic tests, including sensitivity, specificity, and predictive value
Describe receiver operating characteristic curves and their role in diagnostic tests
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Arithmetic mean
Bar chart
Box and whisker plot
Coefficient of variation
Continuous variables
Control variables
Dependent variable
Descriptive statistics
Discrete variables
False negative rate
False positive rate
Frequency table
Histogram
Incidence
Independent variable
Inferential statistics
Interquartile range
Interval data
Mean
Measures of central tendency
Measures of dispersion
Median
Mode
Negative predictive value
Nominal data
Ordinal data
Pie chart
Positive predictive value
Prevalence
Proportion
Qualitative data
Quantitative data
Range
Rates
Ratio data
Receiver operating characteristic (ROC) curve
Sensitivity
Skewness
Specificity
Standard deviation
Statistics
True negative rate
True positive rate
Variable classification schemes
Variance
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In research, measurement is the process of collecting and recording observations about the variables that are of interest in a specified project. These collected observations are called data. There are a variety of tools that researchers can employ to collect observations. Some examples of measurement tools include stadiometers (height), sphygmomanometers (blood pressure), enzyme-linked immunosorbent assays (antibodies in serum), and questionnaires (health status). Once observations are collected, these measurements about the variables of interest (i.e., data) are used to inform the research question, and this occurs through the use of statistics. An editorial in Science defined statistics as “…the science of learning from data, and of measuring, controlling, and communicating uncertainty.”1 Thus, functions of statistics include summarizing, organizing, presenting, analyzing, and interpreting data.2
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This chapter is devoted to the summarizing, organizing, and presenting functions of statistics, commonly referred to as descriptive statistics. Analytic (e.g., hypothesis testing) and interpretation functions, which are collectively referred to as inferential statistics, will be covered in subsequent chapters. This chapter introduces various schemes used to classify and summarize variables so that collections of information can be simplified and communicated in a manner that is both straightforward and standardized. Next, we discuss how to visualize and optimally present data so that it is logical and comprehensible. The chapter concludes with a discussion of summary measures used to convey morbidity and mortality information as well as the summary measures used to describe diagnostic test performance.
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At the most fundamental level, data may be qualitative or quantitative in nature. Qualitative dataa, or meaningful information that is collected in words, may provide valuable insight into the condition of individual patients, but are not typically used for the purposes of healthcare research with large populations of patients.3 Written observations or notes found ...