Experimental Design and Variables

By the end of this lesson, you will be able to identify independent, dependent, and controlled variables, design controlled experiments, and explain why replication and controls matter in biological research.

The Big Idea

Good experimental design is what separates a reliable conclusion from a guess. A well-controlled experiment changes exactly one variable at a time, keeps everything else constant, and repeats the test enough times that statistical patterns emerge. Every major biological discovery — from penicillin's effectiveness to CRISPR's safety — rested on careful experimental design.

Types of Variables

Independent variable (IV): the variable the experimenter deliberately changes; the "cause."

Dependent variable (DV): the variable measured to detect an effect; the "effect."

Controlled variables (CV): all other factors held constant to prevent confounding.

Example 1 — Identifying Variables

Question: Does soil nitrogen concentration affect bean plant height?

• IV: nitrogen concentration (0, 50, 100, 200 mg/L)
• DV: plant height after 4 weeks
• CVs: pot size, water amount, light intensity, temperature, seed variety, planting depth

The Control Group

Experimental group: receives the independent variable.

Control group: a baseline that receives no treatment (or a zero level of the IV). Without a control, you cannot attribute the effect to the IV.

In the bean example: the 0 mg/L group is the control.

Why Sample Size Matters

Biological systems are variable. One bean plant might grow tall due to chance, not nitrogen. With $n = 1$, you cannot distinguish a real effect from random noise.

Larger samples:

• Reduce the impact of outliers
• Allow statistical testing
• Increase confidence in conclusions

Standard in biology: minimum $n = 5$–10 per treatment; AP Bio experiments typically use $n \geq 3$.

Replication

Experimental replication: repeating the experiment multiple times under the same conditions to confirm results.