CHAPTER 10 Having Confidence in Your Results 129

Chapter 10

Having Confidence

in Your Results

I

n Chapter 3, we describe how statistical inference relies on both accuracy and

precision when making estimates from your sample. We also discuss how the

standard error (SE) is a way to indicate the level of precision of your sample

statistic, but that SE is only one way of expressing the preciseness of your statis-

tic. In this chapter, we focus on another way — through the use of a confidence

interval (CI).

We assume that you’re familiar with the concepts of populations, samples, and

statistical estimation theory (see Chapters 3 and 6 if you’re not), and that you

know what SEs are (read Chapter 3 if you don’t). Keep in mind that when you

conduct a human research study, you’re typically enrolling a sample of study par-

ticipants drawn from a hypothetical population. For example, you may enroll a

sample of 50 adult diabetic patients who agree to be in your study as participants,

but they represent the hypothetical population of all adults with diabetes (for

details about sampling, turn to Chapter 6). Any numerical estimate you observe

from your sample is a sample statistic. A statistic is a valid but imperfect estimate

of the corresponding population parameter, which is the true value of that quantity

in the population.

IN THIS CHAPTER»

» ^{Investigating the basics of confidence}

intervals»

» ^{Calculating confidence intervals for}

several different statistics»

» ^{Linking significance testing to}

confidence intervals