Reliability: Sample Size and Repetition
Two experiments can test the same idea and deserve very different levels of trust. What separates a convincing result from a shaky one often comes down to two things: how many subjects were tested (the sample size) and whether the test was repeated. This lesson explains why both matter, because “the study was too small” is one of the most common — and correct — criticisms on the science test.
Why Sample Size Matters
A result from two or three subjects is easy to doubt, because a couple of unusual individuals can swing it entirely. A result from hundreds of subjects is much harder to dismiss, because a few oddballs get averaged out. As the number of observations grows, the outcome settles closer to the true value — the pattern behind the idea that bigger samples give more reliable results.
Picture flipping a fair coin. Flip it four times and you might get three heads, which looks like a \(75\%\) chance of heads. Flip it \(400\) times and the result will sit much closer to the true \(50\%\). The large sample reveals the real pattern; the small sample was just noise.
Why Repetition Matters
Repeating an experiment is the other half of reliability. If a result appears once, it could be a fluke. If the same result appears every time the experiment is run again, you can trust it. Repetition also lets other scientists check the finding for themselves — a result that cannot be reproduced is treated with suspicion. Good experiments are run multiple times, and strong claims rest on results that hold up across repeats.
Reliability on the Test
When a passage describes an experiment, check the numbers. Were only a handful of subjects tested? Was the experiment run just once? If so, the safe response is that the result may not be reliable and needs a larger sample or more trials. When a question asks how to improve an experiment, “use more subjects” and “repeat the trials” are almost always among the best answers. Remember, too, that size fixes randomness but not bias — a sample must be both large and fairly chosen.
Watch: A Short Video Lesson
NStatum walks through this skill clearly in a few minutes. It is a helpful companion to the reading above:
A Routine for Reliability Questions
- Check the sample size — a few subjects is weak; many is stronger.
- Check whether the experiment was repeated.
- To improve reliability, add subjects and repeat trials.
- Remember that a large sample must also be fairly (randomly) chosen.
Practice
- Why is a result from three subjects hard to trust?
- How does a larger sample make a result more reliable?
- Why does repeating an experiment matter?
- An experiment was run once on five plants. Name two ways to improve its reliability.
- Does a large sample fix a biased sample?
- If you flip a fair coin many times, what should the fraction of heads approach?
Answers
- A few unusual individuals can swing the whole result.
- Extreme values get averaged out, so the outcome settles near the true value.
- It shows the result was not a fluke and lets others reproduce it.
- Use more plants and repeat the experiment several times.
- No — bias must be fixed by fair (random) selection, not size alone.
- About one half (50%).
Where This Fits in Your Science Prep
Reliability pairs with samples, populations, and bias and with judging good experiments. Together they tell you how much to trust a study’s conclusion. 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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