Hypotheses and Judging a Good Experiment
A hypothesis is a testable prediction — an educated guess about what will happen, written so that an experiment can actually check it. The best way to think of it is an “if, then” statement: if I change this, then that will happen. On the science test, you will be asked to write hypotheses, match them to experiments, and judge whether an experiment was well designed. All three come from the same core idea.
The good news is that a strong hypothesis and a good experiment follow a small set of rules you can learn once and apply anywhere.
What Makes a Hypothesis Testable
A good hypothesis predicts a clear, measurable result and connects the variable you change to the one you measure. “If plants get more sunlight, then they will grow taller” is testable: you can change the sunlight and measure the height. Compare that to “plants like sunlight,” which is vague and cannot be measured directly.
Notice the shape: if [independent variable changes], then [dependent variable responds]. Writing your prediction in that form almost guarantees it is testable, because it names exactly what to change and what to measure.
Judging a Good Experiment
An experiment is trustworthy when it does a few things well. It changes only one variable at a time. It keeps all other conditions the same. It uses a control group for comparison. And it tests enough subjects, and repeats the trial, so a single fluke cannot decide the result. When an experiment skips one of these, the test may ask you to spot the weakness.
So when you evaluate an experiment, run down the checklist: one variable changed, everything else controlled, a control group present, and enough trials. A missing item is usually the answer to “What is the main flaw in this experiment?”
Bigger Samples, Repeated Trials
Two plants are not enough to trust a result, because one of them might be unusual for reasons that have nothing to do with the experiment. Using many plants in each group, and running the test more than once, makes the outcome far more believable. This is why “the study used only a few subjects” is such a common — and correct — criticism on the test. Bigger, repeated experiments give results you can rely on.
Watch: A Short Video Lesson
The Miacademy Learning Channel walks through this skill clearly in a few minutes. It is a helpful companion to the reading above:
A Routine for Hypothesis and Design Questions
- Write predictions as “if [I change this], then [this will happen].”
- Check that the prediction names something you can measure.
- To judge an experiment, confirm: one variable changed, others controlled, a control group present, enough trials.
- If asked for the flaw, look for the missing item on that checklist.
Practice
- Rewrite “exercise is good for the heart” as a testable if-then hypothesis.
- Name two features of a well-designed experiment.
- Why are two subjects usually not enough?
- An experiment changes the amount of light and the type of soil at once. What is the flaw?
- What does a control group let you do?
- What shape should a testable hypothesis usually take?
Answers
- Sample: “If a person exercises more each week, then their resting heart rate will decrease.”
- Any two of: changes one variable, controls the others, uses a control group, tests many subjects, repeats trials.
- One unusual subject could swing the result; more subjects reduce that risk.
- Two variables changed at once, so you cannot tell which one caused the result.
- Compare the treatment against a baseline that got no change.
- An “if [change], then [result]” statement that names something measurable.
Where This Fits in Your Science Prep
This lesson ties together variables and controls into the full picture of a trustworthy experiment. It also connects to judging data quality through sample size and repetition. See all topics on the Science Topics Hub.
Recommended Prep Books
These study guides and practice books help you keep building momentum as you prepare:
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