Types of Statistics

• Statistics can be divided based on how data is used

Descriptive Statistics

• Collection, organization, summarization, and presentation of data
• Example include the US Census Bureau

Inferential Statistics

• Generalization from samples to a population by performing estimations and hypothesis tests
• Determines relationships among variables
• Makes predictions
• Example include student grades

Population

• Consists of all subjects, human or otherwise, that are being studied

Sample

• A group of subjects selected from a population

Variable Types

• Data is divided into Qualitative and Quantitative types

Qualitative Variables

• Variables that can be placed into distinct categories

Quantitative Variables

• Numerical and can be ordered or ranked
• Can be further classified into two groups, Discrete and Continuous

Discrete Variables

• Assume values that can be counted

Continuous Variables

• Can assume an infinite number of values between any two specific values
• Obtained by measuring and often include fractions and decimals

Variable Classification

• Variables are classified by how they're categorized, counted, or measured using measure scales

Nominal level of measurement

• Classifies data into mutually exclusive non-overlapping categories
• Exhausting categories in which no order or ranking can be imposed on the data
• Example include college instructors classified by subject taught

Ordinal Level of Measurement

• Classifies data into categories that can be ranked
• Precise differences between the ranks do not exist
• Examples include people classified by build or a grading system

Interval Level of Measurement

• Ranks data where precise differences exist between units of measure
• There is no meaningful zero
• Example: IQ result with a meaningful difference of 1 point between an IQ of 109 and 110

Ratio Level of Measurement

• Possesses all characteristics of interval measurement with a true zero
• A true ratio exists when the same variable is measured on two members of the population
• Examples include height, weight, salary, age and time.

Data Collection and Sampling Techniques

• Random sampling is when samples are selected by chance using a chance method or random number

Systematic Sampling

• Numbering each subject of a population, then selecting every kth subject
• Example: Population of 2000 with a needed 50 subjects yields 2000/50 = 40; every 40th subject is selected, randomly starting from the first 40

Stratified Sampling

• Dividing the population into groups or strata, according to characteristics important to the study
• Example: Students from a 2-year college, separated by first and second year students

Cluster Sampling

• Population is divided into clusters
• Selects some of these clusters, and uses all of their members as subjects of the sample
• Example: Patients in NY hospitals

Experimental Studies

• Researcher manipulates one variable to determine how the manipulation influences other variables
• Example: Effect of a pollutant on plant production

Independent Variable

• Manipulated by the researcher in an experimental study
• Also called the explanatory variable

Resultant Variable

• The dependent or outcome variable