What is a True Experiment?
A true experiment is a study where the researcher manipulates at least one variable (the independent variable), randomly assigns participants to groups, and then measures the effect on another variable (the dependent variable).
That is the core. Everything else hangs from these three hooks:
- Manipulation – you change something: treatment vs no treatment, method A vs method B.
- Random Assignment – people (or plants, rats, sensors, classrooms) get placed into groups by chance, not by choice.
- Control/Comparison – you keep a control group or at least a fair comparison group to see what would have happened without the treatment.
Because of random assignment and control, the true experimental method is the gold standard for claiming X caused Y, not just X is associated with Y.
Why Do Researchers Use True Experiments?
Three reasons, mostly:
- Causality, not just correlation: If we randomize well and hold constants steady, differences at the end are very likely due to the treatment.
- Internal validity. This design does the best job blocking threats like selection bias or confounders.
- Clear decisions. You get numbers, you test hypotheses, you decide. keep the new method or ditch it.
Of course, true experiments are not magic. Ethics, cost, time, logistics, these can bite. We will talk limits later.
The Building Blocks of True Experimental Design
1) Variables: IV, DV, and Constants
- Independent Variable (IV): the thing you change on purpose.
- Dependent Variable (DV): the thing you measure.
- Constants (Controlled Variables): stuff you keep the same, so they do not mess with results (temperature, instructions, lighting, timings, etc.).
If constants drift, your inference drifts too. Keep them tight.
2) Random Assignment (Not the same as Random Sampling)
- Random assignment puts participants into groups by chance.
- Random sampling is about how you pick people from the population.
You can have random assignment without random sampling, and many lab studies do. But assignment is the one that protects causality inside the study.
3) Control Group (Or Comparison)
The control group gets no treatment or gets a placebo. Why? Because it shows you the baseline., what would have happened anyway.
4) Blinding
- Single-blind: participants don’t know which group they are in.
- Double-blind: participants and the people who measure do not know.
Blinding reduces expectation effects and observer bias. In education studies it is harder, but you can blind graders or use automated scoring to help.
5) Pre-registration and Protocols
Writing your plan before the study (hypotheses, outcomes, analysis) makes your results more trustworthy. It also keeps you from p-hacking later. Not mandatory for a class project, but a very good habit.
Classic True Experimental Designs (You’ll Meet These in Exams)
- Post-test-Only Control Group Design
- Randomly assign → Give treatment to one group → Measure DV once.
- Simple, clean, avoids pretest sensitization.
- Notation: R X O vs R – O
- Pretest-Post-test Control Group Design
- Measure before and after; good to check groups were similar at start.
- Notation: R O X O vs R O – O
- Solomon Four-Group Design
- Combines both designs to detect pretest effects.
- Notation:
- R O X O
- R O – O
- R – X O
- R – – O
- Yes, it’s big. But very strong.
- Factorial Designs (e.g., 2×2, 3×2)
- Study two IVs at once (say, teaching method and study time).
- You can test main effects and interactions (“Method A works only when study time is high,” etc.).
- Powerful but needs larger sample size.
- Randomized Block / Stratified Assignment
- You group similar participants (blocks) and then randomize inside each block.
- Reduces noise when a known factor (like school grade) affects scores.
- Within-Subjects True Experiments (a.k.a. repeated measures)
- The same participants receive all conditions, but the order is randomized.
- Great for sensitivity but beware carryover and fatigue.
Step-by-Step: How to Run a True Experiment
Let us map this to the four steps of hypothesis testing (you’ll see this in stats classes) while staying friendly.
Step 1: State the Hypotheses
- Null (H₀): The treatment has no effect.
- Alternative (H₁): The treatment does have an effect.
Example: “Music during study does not change test performance” vs “Music does change test performance.”
Step 2: Design & Random Assignment
- Choose a design (post-test-only or pretest-post-test; for classroom studies, pretest-post-test is common).
- Define inclusion/exclusion criteria.
- Randomly assign participants to treatment or control. A quick way: online random number generator or shuffled slips of paper.
Step 3: Collect Data (Keep Constants Steady)
- Deliver the intervention exactly as planned.
- Keep instructions, timing, environment the same across groups.
- Record any deviations; life happens, but transparency matters.
Step 4: Analyse and Decide
- Pick a test that matches the design:
- Two groups, continuous DV: t-test.
- Two+ groups or factorial: ANOVA.
- Report effect size (e.g., Cohen’s d, η²). P-values alone do not tell the whole story.
- Interpret carefully: “Students taught with Method A scored 6.2 points higher on average (d = 0.51, moderate effect).”
Mini-formulas (intuitive view):
t-test ≈ (difference between group means) / (how noisy the data are)
ANOVA F ≈ (variance between groups) / (variance within groups)
Don’t worry if that felt Mathy. The idea is simple: bigger real differences + smaller randomness → stronger evidence.
A Simple Example (Plants & Fertilizer)
- Question: Does Fertilizer X increase plant height?
- IV: Fertilizer X (yes/no).
- DV: Height after 30 days.
- Constants: Same soil, same pot size, same light, same water schedule.
- Design: Post-test-only control group.
- Process: Buy 40 seedlings → randomly assign 20 to Fertilizer X, 20 to plain water → measure height at day 30 → run a t-test.
- Conclusion: If the fertilizer group is taller by a meaningful margin and stats back it up, you’ve got cause-and-effect evidence.
High school friendly, still a true experimental method.
“True Experiment Psychology” — A Concrete Example
Study idea: Do spaced-repetition flashcards improve memory better than massed practice (cramming)?
- Participants: 60 first-year students, recruited from intro psych.
- Design: Pretest-post-test control group.
- Randomization: Randomly assign to Spaced vs Massed.
- Intervention:
- Spaced group studies words across four short sessions.
- Massed group studies the same total time but in one long session.
- DV: Delayed recall score one week later.
- Hypothesis: Spaced > Massed on delayed recall.
- Analysis: Independent-samples t-test (or ANOVA if you add a third group like “no-study”).
- Outcome (imaginary): Spaced mean = 18.4/25, Massed mean = 15.9/25, p = .01, d = 0.55.
- Interpretation: Spaced repetition caused better memory under these conditions. That is the claim you are allowed to make.
Add blinding if you can (e.g., graders who mark recall tests don’t know which group the student was in).
True Experiment vs Quasi-Experiment vs Observational Study
- True Experiment: random assignment + control → highest internal validity.
- Quasi-Experiment: no true random assignment (e.g., intact classrooms). Still useful, but weaker for causal claims; you will rely on matching or statistics to adjust.
- Observational/Correlational: you measure variables as they occur naturally. Great for discovering patterns, not great for proving causation.
If your dean says, “We cannot randomly split classes,” you’ll probably do quasi-experimental design. Still good; just be honest about limits.
Validity: Keeping Your Result Trustworthy
Internal Validity (inside the study)
Threats & fixes:
- Selection Bias: fix by random assignment, or block randomization.
- Maturation/History: keep timelines short, treat and control simultaneously.
- Instrumentation: same instrument & calibration for all.
- Testing Effect: pretests can teach; consider Solomon four-group if worried.
- Attrition: track dropouts; analyse with intention-to-treat where possible.
External Validity (generalizing out)
- Use realistic tasks and settings if you care about real-world use.
- Don’t over-claim: a lab memory test isn’t the whole human memory.
Sample Size and Statistical Power
Small samples are noisy. Large samples are steadier.
- Power is the chance your test will detect a real effect.
- For classrooms: 25–30 per group is a practical starting point.
- For medical or high-stakes studies, you’ll run a formal power analysis (G*Power is common).
Not a rule carved in stone, just a good habit to think ahead.
Learn more about sample size in simple words
Measuring, Scales, and Reliable Instruments
Your measurement must be reliable (consistent) and valid (measures what it claims).
- If you build a test, pilot it first.
- For surveys, report Cronbach’s α (internal consistency).
- For behavioural tasks, define rubrics. Train raters: compute inter-rater agreement if needed.
Garbage in → garbage out. Good measurement is half the experiment.
Ethics (Never Skip)
- Informed consent: Tell participants what the study involves, risks, and that they can withdraw anytime.
- Minimal risk: Don’t harm. Don’t deceive without justification and debrief.
- Privacy: Keep data confidential; anonymize where possible.
- Approval: Schools and universities use an IRB/ethics committee; even classroom projects should follow the spirit of it.
True Experimental Design in Different Fields
- Education: Compare two teaching methods while randomizing at the student or class level. Pretest-post-test works well.
- Psychology: Memory, attention, decision-making, therapy outcome studies. Double-blind when feasible.
- Biology: Fertilizers, growth hormones, light cycles—plants and model organisms are ideal for tight control.
- Medicine: Randomized Controlled Trials (RCTs) are the industry standard for drugs and interventions.
- Engineering/UX: A/B tests of interfaces or algorithms using random assignment to versions.
Same skeleton. Different clothes.
Data Analysis: Which Test When?
- Two groups, one measurement: Independent-samples t-test.
- Same people measured twice: Paired-samples t-test.
- More than two groups or factorial designs: ANOVA (one-way, two-way).
- Post-hoc tests (Tukey, Bonferroni) after ANOVA if you’ve got multiple comparisons.
- Assumptions check normality, equal variances; if not, consider non-parametric (Mann–Whitney, Wilcoxon).
- Always report effect size and confidence intervals. The “how big” is as important as the “is it significant.”
Common Mistakes (and Friendly Fixes)
- Confusing random sampling with random assignment.
→ Assignment is what buys you causality. Sampling affects generalization. - Pretest differences scare you.
→ Use change scores or ANCOVA with the pretest as covariate. - Leaky instructions.
→ Script delivery. Practice it. Keep the tone same. - Ignoring attrition.
→ Track dropouts by group; analyse with and without them. - P-hacking.
→ Pre-register. Decide analyses before peeking. - Too many outcomes.
→ Choose a primary outcome. Mark secondaries as exploratory. - Not reporting nulls.
→ A careful “no effect” is still good science and very useful.
Mini Case Series: Five Example Scenarios
1) Education – Spaced vs Massed (we saw)
- Result: Spaced wins on delayed recall.
- Takeaway: Use spacing for homework planning.
2) Classroom Tech – App A vs App B
- Design: 2×2 factorial (App × Session length).
- Finding (suppose): App A beats App B only when sessions are 20 min+, an interaction.
- Practical: If class periods are short, the fancy app gives no edge; save money.
3) Biology – Light Colour on Plant Growth
- Groups: Red LED vs Blue LED vs White control.
- Analysis: One-way ANOVA + Tukey.
- Outcome: Blue > White, Red ≈ White.
- Interpretation: Blue boosts growth in this species; maybe due to chlorophyll absorption spectra.
4) Psychology – Sleep Restriction and Reaction Time
- Within-subjects: Same participants tested after 8h sleep vs 4h sleep (order randomized).
- Stat: Paired t-test.
- Result: Slower reactions after 4h.
- Note: Counterbalance to reduce order effect.
5) Medicine – New Analgesic vs Standard
- RCT, double-blind, placebo-controlled if allowed.
- Endpoints: Pain score at 2h, rescue medication use.
- Ethics: Strict; safety monitoring.
- Interpretation: Only if clinically meaningful improvement appears, not just statistically tiny.
Frequently Asked Questions
What is a true experiment in simple words?
A true experiment is a study where you change something on purpose (the treatment), randomly assign people to groups, and compare outcomes. It’s the cleanest way to show that the change caused the result.
What’s the difference between a true and a quasi-experiment?
True = random assignment. Quasi = no true random assignment (uses intact groups, matching, or statistical controls). True experiments have stronger internal validity.
Do I always need a pretest?
No. post-test only designs are fine and avoid pretest sensitization. Use pretest-post-test if you want to verify groups were similar at start or to measure gains.
Is random assignment the same as random sampling?
Nope. Assignment decides which group people go into; sampling decides who comes into the study. Causality needs assignment; generalization likes good sampling.
Can I run a true experiment in psychology?
Absolutely. Many classic psychology studies are true experiments: memory, attention, perception, learning—labs love them. Clinical psychology often moves toward RCTs for therapies.
What is the fourth step in the scientific method?
Usually taught as analyse the data (after observation, hypothesis, experiment). Some curricula vary, but “analysis” around fourth is common. Then comes conclusion/communication.
Which statistical test should I use?
Two groups with independent people → independent t-test.
Same people (pre vs post) → paired t-test.
Three or more groups / factorial → ANOVA.
Not meeting assumptions? Consider non-parametric equivalents.
Do I need blinding in education research?
It’s hard to blind students to a method, but you can blind graders or use automated scoring. Even partial blinding reduces bias.
Limitations and Real-World Friction (Honest Talk)
- Ethics/Feasibility: You can’t randomly assign harmful behaviours, obviously.
- Hawthorne effect: People behave differently because they know they are in a study.
- Generalization: Lab tasks may not mirror the messy outside world.
- Attrition: People drop out; if they drop unevenly, interpretations get messy.
- Resource Needs: True experiments can need more participants, more coordination, more time.
Still, when you truly need to show cause, this is the design to beat.
How to Write the “Purpose of the Study” (So Reviewers Smile)
One clear paragraph does the trick:
- Context: one or two sentences of background.
- Gap: what we don’t yet know.
- Aim: what this experiment will test.
- Outcome: primary measure and the direction (if directional).
- Benefit: why the answer matters in practice.
Example:
“Many students cram for exams, but the long-term effects of cramming vs spaced study are debated. This study aims to test whether spaced repetition improves delayed recall relative to massed practice in first-year students. The primary outcome is delayed free recall one-week post-study. If spaced practice improves recall, the result can guide homework policy in large classes.”
you may refer my this article also – Tips for Explaining Science to Non-Scientists: A Complete Guide
Wrap-Up (and a little nudge)
So that is the true experimental method in plain language. You manipulate one thing, you randomize groups, you measure fairly, you analyse straight, and you tell the truth about what you found. It’s careful work, but not mysterious. You can run a compact version in a classroom, lab, garden, or even inside a browser window with an A/B test.