The Scientific Method

Click any step to expand · Science is a cycle, not a checklist

BIO 111 — Chapter 1
1
Observation

You notice something in the world that sparks a question. Good observations are specific, repeatable, and documented. This is where all science begins — not in a lab, but in curiosity.

🔬 A dairy plant example: "The yogurt batch from Tuesday had higher-than-normal yeast counts on the Petrifilm reads. This hasn't happened before."

Qualitative Quantitative Data collection
2
Question

Turn your observation into a focused, testable question. A good scientific question can actually be answered with evidence — it's not philosophical or opinion-based.

🔬 "Does inoculation temperature above 43°C cause abnormal yeast growth in cultured yogurt?"

Must be testable Specific Measurable
3
Hypothesis

A hypothesis is a testable, falsifiable prediction — an educated guess based on prior knowledge. It's written as an if/then statement. A hypothesis is not just a guess; it must be possible to prove it wrong.

🔬 "If yogurt mix is inoculated above 43°C, then yeast counts will exceed 100 CFU/g due to thermal stress on competing bacteria."

If / then format Falsifiable Predicts outcome
4
Experiment

Design a controlled test that isolates variables. You change one thing (independent variable), measure another (dependent variable), and hold everything else constant (controlled variables). A control group gives you a baseline for comparison.

🔬 Run batches at 38°C (control) and 46°C (experimental), same culture, same milk, same incubation time. Plate both on Petrifilm Y&M. Run in triplicate.

Independent variable Dependent variable Controls Replication
5
Data & Analysis

Collect results and analyze them objectively. Look for patterns, calculate averages, note anomalies. Good scientists separate raw data from interpretation — what happened vs. what it might mean.

🔬 Control batches: avg 12 CFU/g. High-temp batches: avg 340 CFU/g. The difference is large and consistent across all three replicates.

Quantitative data Graphs & tables Statistics
6
Conclusion

Did your results support or refute the hypothesis? A good conclusion states this clearly, acknowledges limitations, and suggests next steps. Science never truly "proves" — it supports or fails to support.

🔬 "Data supports the hypothesis. High inoculation temps correlate with elevated yeast counts, consistent with thermal suppression of lactic acid bacteria. Recommend CAPA: validate inoculation temp controls."

Supported or refuted Limitations Next steps
Was the hypothesis supported?
Yes →
Publish, share findings, replicate with larger sample. Other scientists test it too.
No →
Revise hypothesis, redesign experiment. A "no" is still valuable data!
Either way — the cycle continues. Science is never finished.

The real scientific method is messier than this — but this is the framework your exam will test.