PSYC202 Chapter 1 – Introduction to Statistics
1.1 Statistics, Science, and Observations
Statistics: set of mathematical procedures for organizing, summarizing, and
Provides a set of standardized techniques for interpreting information that can be
understood by all scientists
1.2 Populations and Samples
Population: set of all the individuals of interest in a particular study
Populations must be defined by the researcher
Sample: a set if individuals selected from a population, usually intended to
represent the population in a research study
Samples are used to generalize information back to the population
Variables and Data
Variable: a characteristic or condition that changes or has different values for
Data (plural): measurements/observations of variables.
Data Set: a collection of measurements/observations
Datum: singular measurement/observation. Also known as a “raw score”
Difference between data from individuals and data set from scores
Parameters and Statistics
When describing data it is necessary to distinguish whether the data come from a
population or a sample
Parameter: a value (usually numerical) that describes a population. Usually
derived from measurements of the individuals in a population.
Statistic: a value (usually numerical) that describes a sample. Usually derived fro
measurements of the individual in a sample.
Descriptive and Inferential Statistical Methods
Descriptive Statistics: statistical procedures used to summarize, organize and
simplify data (raw scores). Often scores are organized into a table or graph.
Alternatively, an average may be found
Inferential Statistics: consists of techniques that allow us to study samples and
then make generalizations about the populations from which they were selected
Sampling Error: the discrepancy that exists between a sample statistic and its
representativeness of the corresponding population parameter. Each sample
contains different individuals and produces different statistics. Statistics vary
based on the chosen sample and none of these statistics are perfectly
representative of the population.
1.3 Data Structures, Research Methods, and Statistics
2 data structures: Correlational method & Experimental/nonexperimental
Correlational Method: 2 different variables are observed to determine whether
there is a relationship between them o Relationship between numerical data is often displayed in a graph.
Relationship between nonnumerical data is often displayed in a summary
table called a chisquare test
o Comparing 2 or more groups of scores. If numerical data, averages are
usually calculated and compared.
o Experimental Method: manipulating variables to demonstrate a causeand
effect relationship. Manipulation and Control (of extraneous variables) is
o 2 categories of Variables:
Participant Variables: age, gender, sex, intelligence, etc.
Researchers must ensure that participant variables do not differ
from one group to another.
Environmental Variables: lighting, time of day, weather
Whenever a study has more than one potential explanation for the
results, the study is said to be ‘confounded’
o Controlling for extraneous variables:
Matching (ex. Putting people of similar intelligence in one group)
Holding Variables Constant (ex. Holding age constant and only
testing one age group)
o Independent Variable: variable being manipulated. Usually consists of
the 2 treatment conditions to which subjects are exposed. The independent
variable consists of the antecedent conditions that were manipulated prior
to observing the dependent variable.
The independent variable always consists of at least 2
variables. Must have at least 2 different values.
o Dependent Variable: variable being observed to assess the effect of
o Control Condition: group not receiving treatment. Either receives no