Ways of Thinking and Working in Biology

What Makes Biological Thinking Distinctive?

Biology asks questions about living systems that are often more complex and variable than those in physics or chemistry. This shapes how biologists think and work.

Central features of biological thinking include:

System thinking
Organisms are understood as systems with interacting parts (cells, organs, behaviors, environments). Biologists often ask:

How do the parts interact?
What happens to the system if one part changes?

Levels of organization
Biological questions are framed at specific levels: molecules, cells, tissues, organisms, populations, ecosystems. The same phenomenon can be viewed differently at each level (for example, “disease” at the molecular level vs. at the population level).
Historical thinking (evolutionary perspective)
Living structures and behaviors are seen as the result of evolutionary history. A common question is:

Why is this structure like this, and how might it have arisen?

Functional thinking
Structures and processes are often interpreted by asking:

What is this for? (What function does it serve for the organism’s survival and reproduction?)

Probabilistic and statistical thinking
Because living systems vary, biology frequently uses probabilities (likelihoods, averages, distributions) rather than exact, deterministic predictions for individuals.
Context dependence
Biological conclusions often depend on conditions (e.g., temperature, nutrient availability, habitat). Biologists routinely ask under which conditions a statement is true.

These ways of thinking guide how studies are designed, how data are interpreted, and how explanations are formulated in biology.

The Biological Question: From Observation to Hypothesis

Biological work usually begins with a question rooted in observation. A typical chain is:

Observation
Something is noticed in nature, a lab, or an existing dataset
(for instance, “Plants near the path are shorter than those farther away.”).
Descriptive question

What exactly is happening?
Example: How does plant height change with distance from the path?

Explanatory question

Why is it happening? or How does it work?
Example: Does trampling by walkers reduce plant growth?

Hypothesis
A testable, precise statement predicting a relationship or effect.
Example: Frequent trampling reduces plant height by damaging growing tissue.

Biological hypotheses must be:

Specific (clear what is being compared or measured),
Testable (the outcome can, in principle, show the hypothesis to be wrong),
Related to existing knowledge (they usually build on earlier findings or theories).

Core Workflows in Biological Research

Biologists use several broad approaches that often complement one another.

1. Descriptive (Observational) Biology

Here, the aim is to describe living systems as accurately and completely as possible, without manipulating them.

Typical tasks:

Identifying and cataloguing species.
Mapping distributions of organisms.
Recording body structures, behaviors, life cycles.
Monitoring changes over time (e.g., migration timing, flowering dates).

Key features:

Often the first step in exploring little-known groups or habitats.
Provides baseline data that later experiments build on.
Requires careful sampling and standardized methods (e.g., same time of day, same type of trap or counting method).

Descriptive biology is crucial whenever manipulation is difficult, impossible, or ethically unacceptable (for example, studying rare species in the wild).

2. Experimental Biology

Experimental biology tests hypotheses by actively changing one or more factors and observing the effects.

Basic structure:

Independent variable: what is deliberately changed (e.g., light intensity, nutrient supply).
Dependent variable: what is measured in response (e.g., growth rate, enzyme activity).
Control group: treated identically except for the tested factor.

Important principles:

Standardization of conditions (such as temperature, time, method of measurement).
Replication: repeating the experiment with multiple individuals or samples.
Randomization: assigning treatments randomly to avoid hidden biases.

Experimental work can take place:

In the lab (controlled conditions, simpler systems, often fewer variables).
In the field (more realistic conditions but more environmental variability).

3. Comparative and Correlative Studies

Not all questions can be addressed by direct manipulation. Comparative approaches look for patterns by comparing:

Different species,
Different populations,
Different environments or times.

Examples:

Comparing bone structures across vertebrates to infer evolutionary relationships.
Comparing disease rates in populations with different lifestyles or environments.

A special case is correlative studies, which look for statistical relationships between variables (e.g., body size and lifespan across species). Correlation suggests a link but does not, by itself, prove cause and effect.

4. Modeling and Simulation

Biologists also work with conceptual and mathematical models to think about complex systems.

Conceptual models: diagrams and flowcharts describing interactions (for example, food webs).
Mathematical models: equations describing processes such as population growth or the spread of an infection.

Typical uses:

Making predictions (e.g., how a population will change under certain conditions).
Testing whether a set of assumptions could explain observed patterns.
Exploring what might happen under scenarios that are hard to test directly (such as future climate conditions).

Models are always simplifications. They are judged by how well their predictions match real data and whether they help generate new, testable ideas.

Data in Biology: From Measurement to Interpretation

Because biological systems often show variability, data collection and interpretation are central to biological work.

Types of Data

Qualitative data: descriptive (e.g., color, presence/absence of a trait, type of behavior).
Quantitative data: numerical measurements (e.g., length in cm, population size, rate of reaction).

Quantitative data are often summarized with:

A mean (average),
A measure of spread (such as range or standard deviation),
Graphs (line graphs, bar charts, scatter plots) for visual interpretation.

Repetition, Sample Size, and Variation

Individual organisms differ. Biological work must therefore consider:

Sample size: more individuals give a better estimate of the “typical” value and variation.
Natural variation: differences due to genetics, environment, and chance.
Measurement error: inaccuracies due to the method or instrument.

Biologists aim to distinguish meaningful patterns from random variation, often using statistical methods (details belong to other sections but form part of the way of working).

The Hypothesis–Test–Revision Cycle

Biological research rarely ends with a single experiment. Instead, biologists work in cycles:

Formulate a hypothesis based on observations and existing knowledge.
Plan a study:

Choose methods (observational, experimental, comparative).
Decide what to measure and how.
Define controls and treatments.

Collect data:

Follow the plan; keep conditions as consistent as possible.
Record all relevant information, including unexpected problems.

Analyze data:

Summarize results in tables and graphs.
Use appropriate statistical tools to test whether patterns are likely to be real or due to chance.

Interpret results:

Do the findings support, partially support, or contradict the hypothesis?
Are there alternative explanations?

Revise knowledge:

Refine hypotheses or develop new ones.
Design follow‑up studies to test the new ideas.
Integrate results into broader theories (for example, about physiology or evolution).

This cycle is central to how biological knowledge grows and becomes more reliable over time.

Specific Ways of Working in Different Biological Fields

While all biological subdisciplines follow the general scientific approach, each area has characteristic methods and working habits.

Field Biology

Work in natural habitats includes:

Surveys and censuses (counting individuals, mapping locations).
Sampling (for example, taking soil cores, trapping insects with standardized traps).
Mark–recapture or tracking (tagging animals, using GPS or radio transmitters).
Long-term monitoring (repeating the same measurements over many years).

Important aspects:

Careful planning to minimize disturbance.
Respect for legal and ethical guidelines (especially with protected species and habitats).
Dealing with uncontrolled factors such as weather and human activities.

Laboratory Biology

Laboratory work often focuses on controlled, repeatable experiments at smaller scales.

Typical tasks:

Preparing and maintaining cultures of cells, microorganisms, or model organisms.
Dissecting and examining tissues.
Measuring physiological variables (e.g., enzyme rates, membrane potentials).
Using instruments (e.g., microscopes, spectrophotometers) following precise protocols.

Key features of lab work:

Standard operating procedures to ensure consistency.
Detailed lab notebooks documenting every step (date, materials, conditions, observations).
Calibration and maintenance of equipment to ensure reliable measurements.

Microscopy and Imaging

Because many biological structures are too small to see with the naked eye, microscopy is a central way of working.

Working steps often include:

Sample preparation:

Fixing (preserving structure),
Embedding and sectioning (cutting thin slices),
Staining (using dyes that bind specific structures).

Observation:

Adjusting focus, illumination, and magnification.
Systematically scanning fields of view.

Documentation:

Drawing or photographing observations,
Labeling structures and scales clearly.

Careful microscopy requires patience, attention to detail, and consistent technique.

Molecular and Cellular Techniques

Modern biology frequently works at the molecular and cellular level. Typical working patterns include:

Extracting biomolecules (such as DNA, RNA, proteins) from cells.
Separating and identifying molecules (for example, via gels or chromatography).
Manipulating genetic material (cloning, introducing DNA into cells).
Quantifying gene expression or protein levels.

These methods rely on:

Strict temperature control,
Accurate pipetting of very small volumes,
Avoidance of contamination (sterile technique).

Although the specific techniques belong in other sections, the way of working emphasizes precise protocols and repetition.

Thinking About Evidence and Uncertainty

Biological conclusions are always linked to the quality and limits of the evidence.

Key aspects of this way of thinking:

Distinguishing observation from interpretation
Observation: what was actually seen or measured.
Interpretation: what it is thought to mean.
Awareness of limitations

Sample size (few individuals vs. many),
Range of conditions studied,
Precision of instruments.

Alternative explanations
Biologists routinely ask:

Could some other factor explain this result?
Are there confounding variables that were not controlled?

Tentativeness of conclusions
Scientific statements in biology are usually formulated with some caution:

“The results support the hypothesis that…”
“Under the conditions tested, it appears that…”

This reflective attitude is part of responsible biological practice.

Ethical and Responsible Practice in Biological Work

Working with living organisms and environments raises specific responsibilities.

Important considerations:

Animal welfare
Minimizing stress, pain, and harm; using the smallest number of animals needed; following established guidelines and regulations.
Human subjects
When humans are involved (for example in medical or behavioral studies), informed consent, privacy, and safety are essential.
Environmental impact
Avoiding unnecessary damage to habitats, careful use of chemicals, and considering long-term effects of interventions (such as releases of organisms).
Honesty and transparency

Reporting methods and results truthfully, including negative or unexpected findings.
Avoiding plagiarism and falsification.
Sharing data and methods so others can check and build on them.

These practices are not just rules; they shape how biologists think about their work and its consequences.

Communication as Part of Biological Work

Biological knowledge becomes useful only when it is communicated clearly.

Typical forms of communication:

Written reports and papers
Structured presentation of background, methods, results, and interpretation.
Posters and talks
Visual and oral summaries for scientific meetings and teaching.
Graphs, tables, and diagrams
Central tools to present data and models in a way that others can understand and evaluate.

Biologists must:

Distinguish clearly between data (what was measured) and conclusions (what is inferred).
Use precise terminology so that others know exactly what is meant.
Provide enough detail in methods that others could repeat the work.

Thus, thinking and working in biology always includes considering how findings will be understood and scrutinized by others.

Integrating Different Approaches

Modern biological questions often require combining:

Observations in the field,
Experiments in the lab,
Mathematical modeling,
Molecular and physiological measurements,
Historical and evolutionary reasoning.

The way of thinking and working in biology is therefore increasingly interdisciplinary and collaborative. Biologists from different subfields contribute their specific methods and perspectives to build more complete explanations of living systems.