Overview
This lecture covers the use of block design in experimental research to control for unwanted variability and biases, ensuring more reliable and reproducible results.
Block Design in Experimental Research
- Block design divides a large experiment into smaller, manageable sub-experiments called blocks.
- Each block includes all experimental conditions but with fewer replications.
- Blocks can be based on factors like cage, room, building, location, day, time, age, batch, or personnel.
- Within each block, experimental variation is minimized as much as possible.
Reasons for Blocking
- Blocking accounts for unwanted variation that could bias experimental results.
- It helps attribute observed differences to treatments rather than to uncontrolled environmental factors.
- Statistical analysis can include a variable for blocks to control for block-specific effects.
Examples of Blocking
- Animal studies may block by cage to control for environmental differences, like temperature or sunlight.
- Human studies may block by school, class, or socioeconomic status to control for group-specific influences.
Steps for Effective Blocking
- Identify all potential sources of variability relevant to the experiment.
- Consult other scientists and review literature to avoid overlooking important sources.
- Classify sources of variability that may bias results.
- Assign subjects and interventions evenly across blocks to ensure balance.
Importance and Limitations
- Blocking cannot eliminate all sources of bias but helps reduce it for those that can be controlled.
- Proper blocking increases the reproducibility and reliability of research findings.
Key Terms & Definitions
- Block Design — A method dividing an experiment into smaller sub-experiments (blocks) to manage and control variability.
- Bias — Systematic error or deviation from the true effect due to uncontrolled variables.
- Confounder — An outside factor that can influence the outcome and lead to incorrect conclusions if not controlled.
Action Items / Next Steps
- Identify potential sources of variability for your own experiments.
- Review relevant literature and seek input from colleagues about possible biases.
- Plan experimental blocks and allocate subjects and treatments evenly.