10 PART 1 Getting Started with Biostatistics

describes how to collect and validate your data. Then in Chapter 9, we show you

how to summarize each type of data and display it graphically. We explain how to

make bar charts, box-and-whiskers charts, and more.

Drawing Conclusions from Your Data

Most statistical analysis involves inferring, or drawing conclusions about the pop-

ulation at large based on your observations of a sample drawn from that popula-

tion. The theory of statistical inference is often divided into two broad sub-theories:

estimation theory and decision theory.

Statistical estimation theory

Chapter 10 deals with statistical estimation theory, which addresses the question of

how accurately and precisely you can estimate a population parameter from the

values you observe in your sample. For example, you may want to estimate the

mean blood hemoglobin concentration in adults with Type II diabetes, or the true

correlation coefficient between body weight and height in certain pediatric popu-

lations. Chapter 10 describes how to estimate these parameters by constructing a

confidence interval around your estimate. The confidence interval is the range that

is likely to include the true population parameter, which provides an idea of the

precision of your estimate.

Statistical decision theory

Much of the rest of this book deals with statistical decision theory, which is how to

decide whether some effect you’ve observed in your data reflects a real difference

or association in the background population or is merely the result of random

fluctuations in your data or sampling. If you measure the mean blood hemoglobin

concentration in two different samples of adults with Type II diabetes, you will

likely get a different number. But does this difference reflect a real difference

between the groups in terms of blood hemoglobin concentration? Or is this differ-

ence a result of random fluctuations? Statistical decision theory helps you decide.

In Part 4, we cover statistical decision theory in terms of comparing means and

proportions between groups, as well as understanding the relationship between

two or more variables.