Introduction
A
statistic is a numerical representation of
information. Whenever
we quantify or apply numbers to data in order to organize,
summarize, or better understand the information, we
are using statistical methods.
These methods can range from somewhat simple
computations such as determining the mean of a
distribution to very complex computations such as
determining factors or interaction effects within a
complex data set.
This chapter is designed to present an
overview of statistical methods in order to better
understand research results.
Very few formulas or computations will be
presented, as the goal is merely to understand
statistical theory.
Before
delving into theory, it is important to understand
some basics of statistics.
There are two major branches of statistics,
each with specific goals and specific formulas.
The first, descriptive statistics, refers to
the analysis of data of an entire population.
In other words, descriptive statistics is
merely using numbers to describe a known data set.
The term population means we are using the
entire set of possible subjects as opposed to just a
sample of these subjects.
For instance, the average test grade of a
third grade class would be a descriptive statistic
because we are using all of the students in the
class to determine a known average.
Second,
inferential statistics, has two goals: (1) to
determine what might be happening in a population
based on a sample of the population (often referred
to as estimation) and (2) to determine what might
happen in the future (often referred to as
prediction). Thus,
the goals of inferential statistics are to estimate
and/or predict.
To use inferential statistics, only a sample
of the population is needed.
Descriptive statistics, however, require the
entire population be used. Many of the descriptive
techniques are also used for inferential data so
we’ll discuss these first.
Lets start with a brief summary of data
quality.
