Definition: A step-by-step process used by scientists to investigate phenomena, gather evidence, and draw conclusions from experiments & observations.
Purpose: Provides a logical, repeatable, and transparent framework for answering questions about the natural world.
6 Major Steps (in order)
Make an Observation
Ask a Question about the observation
Propose a Hypothesis (testable explanation / prediction)
Design an Experiment to test the hypothesis
Collect Data (measure, record, organize)
Draw a Conclusion based on data analysis
Practical importance
Ensures findings are evidence-based rather than anecdotal
Allows other scientists to replicate work and verify results
Encourages iterative refinement—conclusions can feed back into new observations & questions
Working definition: Testable predictions that can be examined by additional observations or experiments.
Common (but optional) wording: “If … (IV) then … (DV) because … (rationale).”
“If” → manipulated variable = Independent Variable (IV)
“Then” → responding variable = Dependent Variable (DV)
“Because” → optional causal explanation providing biological logic
Results can support or refute the hypothesis, but:
NEVER state “the hypothesis is correct.”
A supported hypothesis is still provisional; future evidence could refute it.
Scientists always start with a null hypothesis as the formal statement to test.
Null Hypothesis (H_0)
Claims no difference / no effect between two groups or treatments.
Serves as the baseline that statistical tests attempt to disprove, reject, or nullify.
Significance: By rejecting H_0, researchers gain confidence that observed patterns are not due to chance alone.
Example: H_0 – “There will be no difference in headache relief between individuals who take Tylenol and those who do not.”
Alternative Hypotheses (H1, H2, H_3, \dots)
State the specific outcomes the researcher expects once H_0 is rejected.
Multiple alternatives can be listed (labeled H1, H2, H_3, etc.) if the experiment can yield several possible effects.
Example alternatives for the Tylenol study:
H_1 – “Tylenol will allow for headache relief when consumed.”
H_2 – “Tylenol will worsen symptoms when consumed.”
Groups & Replication
At least 3 trials per group to obtain reliable averages and support statistical analysis.
Two core group types:
Control Group – Benchmark for comparison; validates that results arise from IV and not external variables.
Experimental Group – Receives the IV treatment to assess its impact on the DV.
Control Sub-types
Negative Control
Group not exposed to the IV OR exposed to a treatment known to have no effect.
Ensures that no effect occurs when none is expected, helps detect contamination or hidden variables.
Positive Control
Group exposed to a treatment known to elicit a specific, expected effect.
Confirms that the experimental setup is capable of detecting an effect at all (i.e., the assay works).
Example – Caffeine & Heart Rate (Negative Control)
Research Question: Does caffeine affect heart rate?
Negative control receives water (known to have no effect).
If water group still shows heart-rate change, researcher must suspect another variable or contamination.
Example – New Antibiotic (Positive Control)
Research Question: Is a new antibiotic effective against a bacterial strain?
Positive control receives a well-established antibiotic known to kill the bacteria.
If new antibiotic groups fail yet the positive control works, the new compound is likely ineffective (setup still valid).
Independent Variable (IV)
The one factor purposely changed between groups.
Graphed on the x-axis.
Dependent Variable (DV)
The factor measured / observed; expected to change in response to the IV.
Graphed on the y-axis.
Constants / Controlled Variables
All other factors kept consistent across groups so that only the IV influences the DV.
Reduce confounding influences and increase internal validity.
Emphasizing H_0 demonstrates scientific objectivity—researchers attempt to disprove their own default assumption.
Proper controls (positive & negative) provide ethical responsibility: ensure no false claims of effectiveness (e.g., pharmaceuticals).
Adequate replication (\ge 3 trials) respects statistical power and reproducibility, defending conclusions from random chance.
[ ] Can you list the 6 steps of the scientific method in order?
[ ] Are you comfortable crafting H0 and at least one H1 for any given research problem?
[ ] Can you distinguish between negative and positive controls and give examples?
[ ] Do you know where IV, DV, and constants appear on a graph?
[ ] Can you explain why “support” ≠ “prove” in scientific language?