Statistics Observational Studies vs Experiments

Study Notes

Data Collection Methods

Two primary methods: Observational Studies and Designed Experiments.

Variables

Explanatory Variable: Changing this variable influences the Response Variable.
Example of Explanatory Variable: A punch.
Example of Response Variable: Degree of pain felt.

Observational Studies

Measure the response variable without influencing any variables.
Researchers simply observe individuals’ behaviors.
Cannot establish causation; only association can be claimed (e.g., cannot definitively say "smoking causes lung cancer").

Designed Experiments

Involve assignment of individuals to groups with intentional changes to the explanatory variable.
Response variables are measured after manipulation (e.g., drug testing with placebo vs. active drug).
Useful when control over variables is needed and feasible.

Confounding Variables

Arise when effects of multiple explanatory variables are mixed.
May misrepresent relationships between variables (e.g., global warming linked with CO2 levels and other factors).

Lurking Variables

Unconsidered explanatory variables that influence the response variable.
Example: In influenza studies, age and health status serve as lurking variables.

Observational Studies Remarks

Cannot make claims of causation.
Only shows correlation or association among variables.

Designed Experiments Remarks

Preferred when control over variables can be maintained.
Less expensive and quicker to conduct than designed experiments in many cases.

Categories of Observational Studies

Cross-Sectional Studies: Collect data at a specific point in time.
Case-Control Studies: Retrospective studies matching individuals with certain characteristics to others without, relying on past information.
Cohort (Prospective) Studies: Follow a group over time; may take years to see results (e.g., long-term studies on cell phone usage).

Cohort Studies Characteristics

Require significant participant commitment over extended periods.
High dropout rates may occur during lengthy studies.
Considered powerful for establishing associations through long-term observation.

Census

Comprehensive data collection from every member of a population.
Example: U.S. decennial census serves as a standard national census.

Description

This quiz explores the key concepts distinguishing observational studies from designed experiments, including definitions and examples. It covers explanatory and response variables, aiding in understanding their interplay in statistical analysis.