The entire group of individuals about which the researcher wants information

Examples:

• All United States Citizens
• All male students at VCU
• All sections of all courses taught this semester at VCU

Some characteristic of the population that the researcher wants to measure

Examples:

1.) Proportion of US citizens who voted in the 2004 presidential election

2.) Average (mean) height of all male students at VCU

3.) Proportion of all sections of all courses taught by adjunct (part-time) faculty

mean is denoted by mu

proportion of successes in a population is denoted by pi

A subset of the population that is contacted and examined to gather information

Example: when the population is all male VCU students, a sample would be all male students in this class

A descriptive measure, computed from data in a sample, that can be expressed or evaluated numerically.

Example: For the sample of male students in this class, one can compute the average height is a statistic

A statement about a population based on the data collected in a sample

Example: To estimate the average height of all male VCU students we can compute the average height of the male students in this class

A listing of all the possible values that a characteristic can take and the number (or percentage) of times that each value occurs

Examples:

1.) Gender of student:

Male____

Female____

2.) Color of car:

Blue____

Red____

White____

Other____

Branch of statistics in which we use data and statistics computed from a sample to make inferences (statements) about a population

The information used to make these inferences is often some type of descriptive statistic

A characteristic whose measurements do not change in repeated trials over time

Example:

1.) Number of days in january each year (always 31)

2.) Number of minutes in an hour (always 60)

A characteristic whose measurements vary(or change) from trial to trial or individual to individual

Examples:

1.) Heights of students

2.) Grades on a test

A variable whose measurements vary in kind or name, but not in degree. This implies that one level of a categorical variable cannot be considered to be greater than or better than another level

Example: 1.) Gender: male or female

2.) Eye color

3.) Social security number

A variable whose measurements vary in magnitude from trial to trial, meaning some order or ranking can be applied to the levels

Examples:

1.) Number of students in a particular class

2.) Weight of a typical student

3.) Grades on a test

A quantitative varible whose measurements can assumer only a countable number of possible values

Example:

1.) Number of students in a particular class

2.) Number of cars in a parking deck

3.) Grades on a test

A quantitative variable whose measurements can assume any one of a countless number of values in a line interval. It is usually a measurable quantity or something that is calculated, such as rates, averages, proportions, and percentages

Examples:

1.) Weight of a typical student

2.) Percentage of students who pass a course

Selection bias occurs when one or more types of subjects are systematically excluded from the sample.

When selection bias exists, the results from the sample can only be inferred to part of the population. the inference cannot be made to the entire population, but only a subset of the population. This is referred to as undercoverage

A haphazard sample involves selecting a sample by some convient mechanism that does not involve randomization.

A mall survey in which questionnaires are distributed to people as they walk through the mall, or a campus survey in which students are questioned as they walk across campus are two examples of haphazard samples.

A volunteer response sample exists when subjects volunteer to be part of the study. Examples include telephone call-in polls, internet surveys, newspaper survey, call in talk show surveys, etc.

The problem with volunteer response samples is that often those who choose to respond often have strong opinions, most often negative opinions, and hence volunteer response samples over-represent those with strong opinions.

samples in which the subjects are chosen randomly to be in the sample are often representative of the population and are, for the most part, free of bias

When each subject of the population has a positive and equal probability of being selected for the sample, then we are using a probability sampling design to select our sample. this will reduce(or eliminate) bias.

1. Label the individuals in the population from 1 to N.

2. Use a table of random digits, like on page 315, to randomly select a sample of n numbers between 1 and N. These numbers correspond to the individuals who are selected to be in the sample.

The population is naturally divided into two or more groups of similar subjects, called strate

Example: Strata 1= males

Strata 2 = females

Simple random samples are then selected from each stratum, and combined to give the complete stratified random sample