Stats Chapter 1

Statistics

A set of mathematical procedures for organizing, summarizing and interpreting information

Population

is the set of all the individuals of interest in a particular study.

Sample

is a set of individuals selected from a population, usually intended to represent the population in a research study.

Variable

is a characteristic or condition that changes or has different values for different individuals.

Data

are measurements or observations.

Data Set

set is a collection of measurements or observations.

Datum

is a single measurement or observation and is commonly called a score or raw score.

Parameter

parameter is a value, usually a numerical value, that describes a population.

Statistic

is a value, usually a numerical value, that describes a sample

Descriptive statistics

Statistical procedures used to summarize, organize, and simplify data

Inferential statistics

Techniques that allow us to study samples and then make generalizations about the populations from which they were selected

Sampling error

Naturally occurring discrepancy, or error, that exists between a sample statistic and the corresponding population parameter

The Correlational Method

Observes two different variables to determine whether there is a relationship between them
Example: Height and Weight

Correlation

When the data consist of numerical scores, the relationship between the two variables is usually measured and described using a statistic called what?

chi-square test

If the measurement process simply classifies individuals into categories that do not correspond to numerical values, what is used?

The Experimental Method

The goal of this study is to demonstrate a cause-and-effect relationship.

Participant Variables

such as age, gender, and intelligence that vary from one individual to another

Environmental Variables

such as lighting, time of day, and weather conditions

Four types of measurement scales

Nominal, Ordinal, Interval, Ratio

Nominal

-Assigns names to variables based on a particular attribute
-Divides data into discrete categories
-No quantitative meaning
Ex: Gender as a variable
-Divided into discrete categories (male and female)
-There is no quantitative meaning...

Ordinal

Has quantifiable meaning
Intervals between values not assumed to be equal
Example: Likert Scales
UNI Teacher Evaluations:
"Does the instructor show interest . . ."
Never
Seldom
Frequently
Always
Has quantifiable meaning
-"Never" is less than "seldom"
-Val

Interval

1. Has quantifiable meaning
Intervals between values are assumed to be equal
2. Zero point does not assume the absence of a value
3.Values do not originate from zero
4. Values cannot be expressed as multiples or fractions
Example: Temperature (Fahrenheit

Ratio

1. Has quantifiable meaning
2. Intervals between values are assumed to be equal
3. Zero point assumes the absence of a value
4. Values originate from zero
5. Values can be expressed as multiples or fractions
6. Has quantifiable meaning
7. Intervals betwee

N=

number for population

n=

number for sample