Scientific Research Methods

Grade 12 · Science · Study Guide

A rigorous overview of the scientific method, experimental design, data analysis, and the principles of valid scientific reasoning.

Scientific research is a systematic process for acquiring knowledge about the natural world. It relies on empirical observation, logical reasoning, and rigorous testing to build reliable explanations.

The scientific method begins with observation and a well-formed question. A hypothesis is a testable, falsifiable prediction about a relationship between variables. An experiment tests the hypothesis by manipulating the independent variable while controlling all other conditions (controlled variables) and measuring the effect on the dependent variable. A control group provides a baseline for comparison.

Statistical analysis is critical for interpreting data. Measures of central tendency (mean, median, mode) summarize data; measures of spread (range, standard deviation, variance) describe variability. The p-value indicates the probability of obtaining results at least as extreme as observed if the null hypothesis were true; a p-value below the significance level (commonly 0.05) leads to rejecting the null hypothesis. Correlation measures the strength of a relationship between variables, but does not imply causation.

Peer review is the evaluation of research by independent experts before publication, a cornerstone of scientific quality control. Replication — repeating experiments independently — distinguishes reliable findings from chance results. Bias (sampling bias, confirmation bias, observer bias) must be identified and minimized. Ethical guidelines govern research involving human or animal subjects, including informed consent and minimizing harm.

Key terms

Hypothesis: A specific, testable, and falsifiable prediction about the relationship between variables in a scientific investigation.
Independent variable: The variable deliberately changed or manipulated by the researcher to observe its effect on the dependent variable.
Control group: The group in an experiment that does not receive the experimental treatment; it provides a baseline for comparison.
p-value: The probability of obtaining results at least as extreme as those observed, assuming the null hypothesis is true; used to assess statistical significance.
Peer review: The evaluation of a scientist's research by independent experts in the field before publication, ensuring quality and validity.
Confounding variable: An extraneous variable that correlates with both the independent and dependent variables, potentially distorting results.

Flashcards

Null hypothesis (H₀): The default assumption that there is no effect or no relationship between variables; what the experiment tries to disprove.
Statistical significance: A conclusion that an observed result is unlikely to have occurred by chance alone, typically when p < 0.05.
Dependent variable: The outcome variable that is measured in an experiment; it may change in response to manipulations of the independent variable.
Placebo effect: A measurable improvement in a patient's condition due to their expectation of receiving a treatment, not the treatment itself.
Sample bias: A systematic error introduced when the sample does not accurately represent the target population.
Replication: The repetition of an experiment by independent researchers to verify the reliability and generalizability of results.
Operational definition: A precise, measurable definition of how a variable will be observed or measured within a specific study.
Meta-analysis: A statistical technique that combines results from multiple independent studies on the same question to produce a stronger overall estimate.

Questions

A researcher finds a strong positive correlation (r = 0.92) between ice cream sales and drowning rates. What is the most accurate conclusion?
1. Ice cream consumption causes drowning
2. Drowning causes people to buy ice cream
3. A confounding variable (hot weather) likely explains both
4. The sample size was too small to be meaningful
Show answer
Answer: A confounding variable (hot weather) likely explains both
Correlation never proves causation; in this classic example, hot weather (confounding variable) increases both swimming/outdoor activity (raising drowning risk) and ice cream consumption simultaneously.
A theory in science is an untested guess or speculation about how something works.
Show answer
Answer: False
A scientific theory is a well-tested, well-substantiated explanation of some aspect of the natural world, supported by a large body of evidence — not mere speculation.
In a double-blind study, neither the participants nor the _____ know who is receiving the treatment.
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Answer: researchers
In a double-blind design, both participants (who don't know treatment vs. placebo) and researchers (who don't know which group is which during data collection) are kept unaware, eliminating placebo and observer bias.
What does it mean for a hypothesis to be 'falsifiable'?
1. It can be proven definitively true by experiment
2. It can be shown to be false by observation or experiment if it is wrong
3. It must be tested by at least three independent labs
4. It must use quantitative measurements
Show answer
Answer: It can be shown to be false by observation or experiment if it is wrong
Falsifiability (Popper's demarcation criterion) means a hypothesis makes predictions specific enough that an experiment or observation could, in principle, show the hypothesis to be wrong.
Which of the following best represents a Type I error in hypothesis testing?
1. Failing to reject a false null hypothesis
2. Rejecting a true null hypothesis (false positive)
3. Accepting an alternative hypothesis without sufficient evidence
4. Collecting an insufficient sample size
Show answer
Answer: Rejecting a true null hypothesis (false positive)
A Type I error is a false positive — rejecting the null hypothesis when it is actually true. The significance level (α) sets the acceptable probability of committing a Type I error.

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