Experimental Design Overview Quiz

Experimental Design Overview

• Experimental design is a structured process used to test hypotheses and investigate cause-and-effect relationships.
• The goal is to create controlled conditions to minimize external influences.
• Findings from well-designed experiments can support scientific laws and theories.
• Scientific theory explains why phenomena occur based on repeated experiments.
• Scientific law describes a pattern often expressed through equations.
• Experimental design is used across all scientific disciplines.

Key Variables

• Independent variable: The variable manipulated by the experimenter.
• Dependent variable: The variable measured, affected by the independent variable.
• Control variable: Variables kept constant to isolate the effect of the independent variable.

Good Experimental Design Principles

• Background knowledge: Essential understanding of the topic.
• Appropriate equipment: Accurate and precise instruments are needed.
• Accuracy: How close measurements are to the true value.
• Precision: How close multiple measurements are to each other.

The Scientific Method

• Experiments are performed following the scientific method to allow replication and verification.
• Existing theories inform the design of good experiments.
• Experiments in turn can lead to new theories.

Experimental Design Steps

• Define variables (independent, dependent, control).
• Formulate a hypothesis: An educated prediction about the outcome.
• Design the experiment (including treatment and control groups).
• Assign subjects: Random assignment is a key element to avoid bias.
• Measure the dependent variable: Collect quantitative or qualitative data.

Experimental Groups

• Treatment Group: Receives the experimental treatment (manipulated variable).
• Control Group: Does not receive the treatment; serves as a baseline for comparison.

Data Analysis

• Data must be accurate and precise to be valid.
• Analysis involves creating and interpreting graphs and tables.
• The interpretation considers what the data says and its implications.

Example: Plant Growth Experiment

• Independent variable: Light color (red, green, yellow, blue).
• Dependent variable: Plant growth (height).
• Control: Normal sunlight exposure.
• Subjects: Plants assigned randomly to different light conditions.

Example of Real-world Application

• Pharmaceutical companies often conduct experiments to show effectiveness of a medicine.
• The placebo group receives a treatment that has no effect on the condition.
• The goal is to see if the treatment is effective.