Information gathered using the senses or instruments, often revealing patterns or anomalies that can lead to scientific questions; can be qualitative or quantitative.

A focused, testable question about the natural world that can be answered by collecting and analyzing data.

A tentative, testable, and falsifiable explanation for an observation, grounded in prior knowledge and reasoning.

The factor that is deliberately changed or manipulated in an experiment to test its effect on the dependent variable.

A tentative, testable explanation for an observation, which can be supported or refuted by evidence.

A specific, measurable statement about expected outcomes if a hypothesis is correct, often in if–then form.

Information obtained through the senses or instruments, forming the starting point for scientific inquiry.

The use of multiple independent samples or subjects in each group to assess variability and increase confidence in results.

From Questions to Experiments: How Biologists Do Science — BIO 204: Fundamentals of Scientific Inquiry in the Biological Sciences

From Curiosity to Research Question

Observations: The Starting Point

Biological experiments begin with observations: patterns, anomalies, or problems in nature or existing data. These can be qualitative (descriptive) or quantitative (measurable), like plant height or cell counts.

From Observation to Question

A scientific question should be clear, focused, and measurable. Instead of asking "Why are some plants better?", ask "Does light intensity affect the growth rate of bean plants?"

What Makes a Question Testable?

A testable question allows you to collect data that could support a yes or no answer. "Do these bacteria divide faster at 37°C than at 25°C?" is testable because you can count cells over time.

Key Idea

Good biological questions link what you observe to something you can measure. This bridge from observation to measurement is what makes scientific investigation possible.

Hypotheses vs Predictions: Getting the Logic Right

What Is a Hypothesis?

A hypothesis is a tentative explanation for an observation, grounded in prior knowledge and reasoning. It must be specific, testable, and falsifiable. Example: Higher light intensity increases the growth rate of bean plants.

What Is a Prediction?

A prediction is a measurable outcome expected if the hypothesis is correct. Example: If light intensity increases growth rate, then bean plants under high light will be taller after 3 weeks than plants under low light.

Multiple Hypotheses

Biologists often propose alternative hypotheses. For tall window plants: H1: more light; H2: higher temperature; H3: less competition. Each explanation leads to different predictions and experiments.

Common Confusions

A hypothesis is not a random guess; it is reasoned. A prediction is not the same as a hypothesis; it is an if–then statement that translates the hypothesis into something you can measure.

Example Walkthrough: Caffeine and Heart Rate

Observation and Question

Observation: Students report faster heartbeats after coffee. Question: Does caffeine consumption affect heart rate in humans? This is measurable using beats per minute (bpm).

Hypothesis

Hypothesis: Caffeine intake increases resting heart rate in healthy adults. This is a specific, testable explanation based on knowledge that caffeine is a stimulant.

Prediction

Prediction: If caffeine increases resting heart rate, then the average resting heart rate 30 minutes after a caffeinated drink will be higher than after a decaffeinated drink in the same healthy adults.

Why This Works

The hypothesis explains why heart rate might change; the prediction specifies what will be measured, when, and between which conditions, allowing for statistical comparison.

Variables in Biological Experiments

Independent Variable

The independent variable is what you deliberately change. In the caffeine example, the IV is caffeine intake: caffeinated vs decaffeinated drink, or different caffeine doses.

Dependent Variable

The dependent variable is what you measure. In the caffeine example, the DV is resting heart rate, measured in beats per minute after consuming the drink.

Controlled and Confounding Variables

Controlled variables are kept constant (time of day, drink volume, waiting time). Confounding variables change with the IV and can mislead results, like testing caffeine only in the morning and decaf only at night.

Transparency and Reproducibility

Modern biology stresses pre-registering variables, reporting all measured factors, and clearly separating planned from exploratory analyses to improve reproducibility and reduce bias.

Identify Variables: Quick Practice

Try classifying variables in this scenario.

Scenario

A biologist wants to know whether fertilizer type affects tomato yield.

They grow tomato plants in pots in a greenhouse.
Group A receives organic fertilizer.
Group B receives synthetic fertilizer.
All plants receive the same amount of water and light.
After 12 weeks, they measure the total mass of tomatoes produced per plant.

Your task

Without looking back, identify:

Independent variable
Dependent variable
Two controlled variables

Reveal the answers when you are ready.

Answers

Independent variable: fertilizer type (organic vs synthetic)
Dependent variable: total mass of tomatoes produced per plant
Controlled variables (examples):

Amount of water
Light intensity and duration
Greenhouse temperature
Pot size

Reflect: How would results be affected if pot size were not controlled?

Controls and Experimental Design in Biology

Control vs Experimental Groups

Control groups provide a baseline and do not receive the experimental treatment (e.g., decaf drink). Experimental groups receive the treatment or different doses (e.g., caffeinated drinks).

Negative and Positive Controls

Negative controls should show no effect, revealing background noise. Positive controls should show a known effect, confirming that the system and methods are working properly.

Replication and Sample Size

Replicates are multiple individuals or samples per group. Larger, justified sample sizes reduce random variation and increase statistical power, which modern biology expects you to report transparently.

Randomization and Blinding

Randomizing group assignment reduces selection bias. Blinding participants or experimenters to group identity reduces expectation bias and strengthens the credibility of biological results.

Check Understanding: Hypotheses, Predictions, Variables

Answer this question to test your understanding.

A researcher observes that a certain algae species grows faster in water collected near agricultural fields than in water from an upstream forested area. They propose: "Higher nitrate concentration increases the growth rate of this algae species." They then state: "If nitrate increases growth, then algae grown in water with added nitrate will have higher cell densities after 5 days than algae grown in water without added nitrate." Which statement is the prediction?

"Higher nitrate concentration increases the growth rate of this algae species."
"If nitrate increases growth, then algae grown in water with added nitrate will have higher cell densities after 5 days than algae grown in water without added nitrate."
Both statements are predictions.

Show Answer

Answer: B) "If nitrate increases growth, then algae grown in water with added nitrate will have higher cell densities after 5 days than algae grown in water without added nitrate."

The first statement is a hypothesis: a proposed explanation for faster growth (nitrate increases growth rate). The second is a prediction: a specific, measurable if–then statement about what will happen in an experiment if the hypothesis is correct.

The Scientific Method in Biology: Iterative, Not Linear

The Biological Research Cycle

Biology follows an iterative cycle: observation, question, hypothesis, prediction, experimental design, data collection, analysis, interpretation, communication, and revision. It is flexible, not a rigid checklist.

Key Nuances

Good hypotheses are falsifiable. Data can support or fail to support a hypothesis but rarely prove it. Considering multiple working hypotheses helps reduce bias in biological research.

Reproducibility and Communication

Modern biology emphasizes reproducibility: others should be able to repeat your work. Clear communication in lab notebooks, preprints, and peer-reviewed papers is essential for cumulative progress.

Design a Mini Experiment: Practice Scenario

Apply what you have learned to sketch a simple biological experiment.