Experimental Design Basics

Experimental Design Essentials

• Formulate a clear hypothesis, such as the effect of banana extract on Salmonella typhae growth.
• Hypotheses can indicate potential outcomes: inhibition, toxicity, or no effect (null).
• Maintain confidence in the hypothesis while remaining adaptable to outcomes.

Controls in Experiments

• Implement a Negative Control, e.g., solvent and Salmonella typhae culture with no treatment.
• Include a Positive Control using a known antibiotic with the same solvent and culture.
• Establish Treatment Conditions with various concentrations of the extract alongside the culture.
• Ensure at least three replicates for each condition to validate results.

Source of Variability

• Recognize that using organisms from different sources can introduce variabilities in experiments.
• Maintain consistency in the source, age, and dealer of test organisms to avoid confounding data.

Data Management

• Prepare templates for real-time data collection, including spectrophotometric readings or physical measurements.
• Use software like Excel for managing data templates, ensuring physical copies are easily accessible.
• Log data promptly and seek supervisor verification to maintain accuracy.

Contingency Planning

• Develop a backup plan to address potential experimental failures or delays in execution.
• Assess logistics, costs, and availability in advance if outsourcing any experimental components.

Data Processing and Sampling Strategies

• Process raw data promptly instead of storing it long-term, adopting efficient folder management techniques.
• Determine a robust sampling plan to statistically evaluate populations, especially when assessing disease prevalence in plants or animals.
• Analyze all data statistically to enhance experiment validity.

Discussion Points for Consideration

• Evaluate the design of the experiment and expected hypotheses thoroughly.
• Consider potential inefficiencies in replicates and their impact on experimental outcomes.
• Draft simple data templates in Excel for effective data capturing.
• Identify possible sources of variation within the experimental setup.
• Understand what outsourcing experiments/samples entails and its implications.
• Differentiate between raw data (initial data collected) and processed data (analyzed, formatted data).
• Explore methods of sampling and its relevance to empirical research.
• Grasp the importance of instrument calibration in ensuring accuracy of experimental results.

This quiz covers the fundamentals of experimental design, including hypothesis formation, control conditions, and instrument calibration. Participants will explore how to set hypotheses, evaluate outcomes, and understand the importance of controls in experimentation.