Experiments (1)
Experiments Overview
Basics of Experiments
Definition: Controlled actions or observations to test hypotheses.
Purpose: Understand causal relationships between variables.
Types of Experimental Design
Randomized Controlled Trial (RCT)
Participants assigned randomly to treatment/control groups.
Aim: Eliminate biases in the assignment and outcomes.
Experimental Terminology
Intervention: Treatment applied in a study.
Control Group: Receives no intervention or a placebo.
Outcome: Measurable result of the intervention.
Variables in Experiments
Independent Variable (IV): The variable manipulated or altered by the researcher.
Dependent Variable (DV): The outcome affected by manipulation of the IV.
Extraneous Variables: Factors not controlled that could influence the DV.
Confounding Variables: Overlap with IV affecting the DV causing misleading results.
Factorial Design in Experiments
Factorial model explains the interaction of multiple IVs on the DV.
Example 2x2 design with two factors, one being gender (male, female) and the other being treatment (placebo, drug).
Data Interpretation and Noise
Statistical Noise: Random fluctuations in data that can obscure true patterns.
Affects the reliability of results. Significant for understanding variability in responses.
Polling and Survey Distortions
Problems arise in surveys such as question misinterpretation, bias in respondent demographics, and refusal to participate.
Polling Errors: Reflections of discrepancies in election polling represented through various studies.
Experimental Validity
Internal Validity: Assures that the study accurately measures what it intends to measure.
External Validity: Determines the generalizability of findings beyond the study context.
Conclusion of Experimental Analysis
Essential to have rigorous controls like random assignment and appropriate blinding.
Importance of understanding all variables to accurately analyze results.
Need for both statistical and practical significance in interpretating data.