Statistics Chapter 1
1.1 An Overview of Statistics
DATA consists of information from observations, counts,
measurements or responses.
STATISTICS is the science of collecting, organizing, analyzing and
interpreting data in order to make decisions.
Two types of data sets:
A POPULATION consists of all outcomes that are of interest. A
parameter is a numerical descriptions of a population.
A SAMPLE is a subset of a population. A statistic is a numerical
description of a sample.
Statistics can be divided into two main branches.
DESCRIPTIVE statistics involves the organization, summarization and
display of data.
INFERENTIAL statistics involves using a sample to draw conclusions
about a population. This involves PROBABILITY, the chance of an
event occurring.
1.2 Data Classification
Types of data:
QUALITATIVE data consists of attributes, labels or other nonnumeric
entries.
QUANTITATIVE data consists of numerical measurements or counts.
Levels of Measurement describe how variables are categorized,
counted, or measured, and determine which statistical calculations
are meaningful.
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NOMINAL level – Used for qualitative data only. Data categorized
using names, labels or qualities. No mathematical computations can
be made at this level.
ORDINAL level – Used for qualitative or quantitative data. Data can
be arranged in a meaningful order, but precise differences between
data entries do not exist.
INTERVAL level – Used for quantitative data only. Data can be
ordered and precise differences between entries can be calculated.
There is no meaningful zero; a zero entry is just a position on a scale.
RATIO level – like the interval level, but there is a true zero and
ratios exist (one data value can be meaningfully expressed as a
multiple of another).
1.3 Experimental Design
Methods of Data Collection
Observational Study
Experiment
Simulation
Survey
Sampling Techniques
RANDOM – Use chance methods or random numbers. Each
subject has an equal chance of being selected.
STRATIFIED – Divide the population into groups (strata) based
on a characteristic important to the study, then select some
members from each group.
CLUSTER – Divide the population into groups based on
proximity, then select all members in one or more groups.
SYSTEMATIC – Select every nth subject.
There are many other sampling techniques, including the
CONVENIENCE sample.
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