(143) NHST3

Testing Relationships in Research

Observational Design
- Researchers act as passive observers, collecting data without manipulating any environmental variables.
- This establishes the presence of relationships but does not indicate causation between variables.
- Can only identify if there's a correlation, not whether one variable causes the other.
Difference Between Experimental and Observational Designs
- In experimental design, causal claims can be made when reporting a correlation.
- Causal claims in observational studies are not possible due to lack of manipulation.
- Important to differentiate between results reporting and study design; observational designs cannot make causal claims.

Examples of Spurious Correlations
- Scripps National Spelling Bee & Spider Deaths
  - Correlation found between the number of letters in the winning spelling bee word and fatalities from venomous spiders.
  - Indicates a strong correlation but no real relationship between the two.
- U.S. Spending on Science & Suicide Rates
  - Correlates U.S. spending on science with suicides by hanging, strangulation, and suffocation.
  - Despite correlation, these variables are completely unrelated.
- Cheese Consumption & Deaths by Bed Sheet Entanglement
  - Correlation exists between per capita cheese consumption and deaths from bed sheets.
  - Again, no real relationship exists, showcasing the need for caution in interpreting correlations.

Key Features of Experimental Design
- Sample Selection: Aims for a random sample to represent the population.
- Group Assignment: Participants are assigned to experimental and control groups through random assignment, minimizing bias.
- Independent and Dependent Variables
  - Experimental group receives treatment (independent variable).
  - Control group does not receive treatment.
Random Assignment Importance
- Ensures that differences in outcomes are attributed to the treatment rather than preexisting characteristics.
- Allows researchers to make causal inferences based on observed differences in dependent variables.

In observational research,
- No manipulation or random assignment; only measurements are taken.
- Correlational statements can be made when sufficiently good random sampling is employed.
- However, conclusions cannot account for causation.
Challenges of Non-Random Sampling
- Many psychology studies don't achieve true random sampling.
- Varied access to participants can limit representativeness of the sample.

Careful Distinction:
- Essential to differentiate between random assignment and random sampling when making causal claims.
- Effective random sampling and random assignment lead to generalizable and causal conclusions, respectively.
- Insights on statistical methods will be further explored in relation to an example experiment in the next section.