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Chapter Objectives

When you have completed this chapter, you will be able to understand:

  • The concept of statistical inference and its components

  • Populations and samples and their relation in statistical inference

  • The different types of sampling techniques

  • The components of statistical inference

  • Probability and hypothesis testing in inferential statistics

  • Errors and limitations in hypothesis testing

  • The concept of estimates—point estimate, interval estimate, and confidence interval

  • Use of confidence interval in statistical inference

  • Systematic and random errors and how to avoid them

  • Validity and reliability and their application in research

In Chapter 9, we discussed the importance of statistics in medical science and introduced the concepts of variables and data. The different types of data, the various data measurement scales, and the different formats of data presentation were discussed, followed by an introduction to descriptive statistics. Descriptive statistics deals with the organization and summarization of the raw data and the concepts of central tendency and dispersion of data. Measures of central tendency tell us about the average value of a set of data, while measures of dispersion provide information about the amount of variability present in a data set—that is, the variety exhibited by the data.1 In short, descriptive statistics help in organizing, summarizing, and appropriately analyzing the data so that one can draw general conclusions about a large body of data from a small amount of data—that is, draw inference from the sample about the population. This and the subsequent chapters of the book will deal with the concepts and application of statistical inference in health science research.


Population and Sample

The term population refers to the aggregate or totality of all the possible individuals, animate or inanimate, with respect to certain characteristics under study. The term sample is used to describe the subset of the population that is selected in order to study the characteristic(s) of the population under consideration.

Statistical Inference

Statistical inference, or inferential statistics, is the process by which a conclusion about a population is reached based on the information contained in a sample that is drawn from that particular population.1 It comprises two broad parts—hypothesis testing and estimation.

Descriptive Measure

The ability to summarize any data by means of a single number is known as a “descriptive measure;” it may be computed either from the data of a population or from the data of a sample.1

Parameter and Statistic

A parameter is a descriptive measure in a population and is computed from all the observations on the population. Examples include population mean, population standard deviation, population median, and so on. Likewise, a statistic is a descriptive measure describing a sample and is computed from the observations contained in a ...

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