Nature of Science — Study Notes

Learning Outcomes

  • On completing this chapter you should be able to …
    • appreciate how scientists work and how scientific ideas are modified over time.
    • recognise that science is a global enterprise that relies on –
      • clear communication
      • peer review
      • reproducibility
      • internationally-agreed conventions.
    • conduct independent research on scientific issues, comparing primary and secondary sources, identifying missing detail or bias.
    • pose appropriate scientific questions and turn them into testable hypotheses.
    • design, plan and conduct investigations with due regard for reliability, accuracy, precision, error, fairness, safety, integrity and equipment choice.
    • produce qualitative/quantitative data, identify patterns, handle anomalous observations and justify conclusions.
    • review the skills used in an investigation and apply them to new, unfamiliar problems.
    • present findings in formats fit for purpose/audience, employing correct scientific terminology, tables, graphs and other representations.
    • critically evaluate media-based arguments involving science and technology.
    • research and present the contributions of named scientists and assess the social impact of their discoveries.
    • appreciate that models are simplified, assumption-laden representations that are refined as new data arrive and whose chief value is predictive.
    • interpret biological phenomena through the lenses of
      • systems
      • interdependence, unity & diversity
      • form-fits-function
      • transfer of information, matter & energy.

Scientific Knowledge & Collaboration

  • Science is not a static list of facts; it is an evidence-based process.
  • As new evidence emerges, theories are revised, refined or rejected.
  • Global collaboration maintains standards by …
    • Clear communication – journal articles, preprints, conferences.
    • Peer review – manuscripts critiqued by experts prior to publication.
    • Reproducibility – identical methods should return similar results when repeated independently.
    • International conventions – e.g. SI units, IUPAC nomenclature, bio-safety levels.
  • Historical example (implied): the shift from spontaneous generation to cell theory illustrates how evidence overturns older ideas.

Data: Types & Sources

  • Qualitative data – descriptive, categorical (e.g. eye colour, presence of a trait).
  • Quantitative data – numerical, may be discrete or continuous (e.g. mass in g\text{g}, height in cm\text{cm}).
  • Primary data – collected first-hand by the investigator.
  • Secondary data – obtained from books, articles, databases; must be evaluated for reliability.

Recognising & Minimising Bias

  • Bias arises when data are not presented objectively.
    • Deliberate bias – vested interests, funding sources with an agenda.
    • Unintentional bias – poor experimental design, confirmation bias.
  • Questions to ask: Who performed the research? Who funded it? What methods were used?

Evaluating Secondary Sources – the TRAAP Test

Time • Relevance • Authority • Accuracy • Purpose

  • Time – how current is the information?
  • Relevance – does it address your question?
  • Authority – qualifications/affiliations of author or publisher.
  • Accuracy – evidence provided? peer-reviewed?
  • Purpose – to inform? persuade? sell?

Investigations: More than Experiments

  • Not all investigations involve lab work – may include field observations, modelling, systematic reviews.
  • Common experimental workflow:
    1. Observation & background research.
    2. Scientific question.
    3. Hypothesis formation.
    4. Prediction (often using an if … then structure).
    5. Experimental design.
    6. Data collection & analysis.
    7. Conclusion & communication.

Crafting a Scientific Question & Hypothesis

  • Good questions are …
    • Focused (clearly defines variables).
    • Testable (empirical evidence can answer it).
    • Ethical & feasible (do-able with available resources).
  • Example: “How does light intensity affect the rate of photosynthesis in Elodea?”
  • Hypothesis: “Increasing light intensity will increase the rate of photosynthesis up to a point, after which the rate plateaus.”
  • Prediction: “If Elodea is exposed to 0,25,50,75,100 W m−20, 25, 50, 75, 100\ \text{W m}^{-2} of light, the volume of O2\text{O}_2 produced in 10 min will increase linearly until 50 W m−250\ \text{W m}^{-2} and then level off.”

Designing, Planning & Conducting Experiments

  • Factors to consider:
    1. Variables
    • Independent variable – intentionally manipulated (light intensity).
    • Dependent variable – measured response (rate of O2\text{O}_2 production).
    • Controlled variables – kept constant (temperature, CO2_2 concentration, plant species).
    1. Safety – undertake a risk assessment: identify hazards → precautions.
    2. Suitable equipment – choose apparatus giving adequate accuracy & precision (e.g. digital thermometer ±0.1∘C0.1^\circ\text{C}).
    3. Accuracy vs. Precision
    • Accuracy – closeness to the true value.
    • Precision – repeatability/consistency of measurements.
    1. Error
    • Random error – unpredictable fluctuations (e.g. ambient temp changes).
    • Systematic error – consistent offset (e.g. un-zeroed balance giving all masses +0.5 g+0.5\ \text{g}).
    1. Reliability – repeat tests, large sample size nn, replicate the experiment.
    2. Fairness – only the independent variable influences the dependent variable; use a control lacking the independent variable.
    3. Integrity – report exactly what occurred, even unexpected results.

Analysing Data

  • Tables – organise raw values, include units & uncertainties.
  • Graphs
    • Line graphs – continuous data (time, temperature).
    • Bar charts – one variable categorical.
    • Label axes, include error bars (± standard deviation σ\sigma) where relevant.
  • Identifying patterns, correlations, causation. Note anomalous points, assess whether to exclude (with justification).
  • Statistical tests (t-test, χ2\chi^2) may be needed to confirm significance.

Drawing Conclusions & Communication

  • State whether the data support or refute the hypothesis.
  • Provide biological explanation (e.g. light saturation of chlorophyll).
  • Suggest improvements & further work – refine variables, longer duration.
  • Communicate via …
    • Written lab report, journal article.
    • Poster or oral presentation.
    • Digital formats (eBook, infographic, video abstract).

Science in Society

  • Science in the Media – news outlets, blogs & social media often simplify or sensationalise findings.
    • Always trace claims back to the primary literature.
  • Impacts of Science – any advance should be assessed for …
    • Economic effects (costs, jobs, market shifts).
    • Social effects (healthcare, quality of life).
    • Sustainability (resource use, environmental impact).
    • Ethical issues (privacy, animal welfare, equity).
  • Example: CRISPR gene editing – potential cures vs. ethical debate on germline modification.

Explaining Biological Phenomena

  • Models – diagrams, equations, simulations. Useful because …
    • simplify complex systems.
    • test predictions cheaply & ethically.
    • get refined with new data.
  • Core conceptual themes:
    1. Systems thinking – organelles → cells → tissues → organs → organisms → ecosystems.
    2. Interdependence & Diversity – food webs, microbiomes, symbiosis.
    3. Form fits Function – bird wing bones are hollow (lightweight, flight), neuron’s long axon (rapid conduction).
    4. Transfer of Information, Matter, Energy – DNA replication, nutrient cycles, trophic levels where only ~10%10\% of energy transfers upward between levels.

Additional Skills & Resources (Section 1.1 – 1.6)

  • 1.1 Evaluating Media-Based Arguments – apply critical thinking & TRAAP.
  • 1.2 Ethics in Scientific Investigations – informed consent, animal welfare regulations.
  • 1.3 Impact of Science on Society – case studies (vaccination programmes, climate modelling).
  • 1.4 Accessing Journal Abstracts Online – PubMed, Google Scholar, institutional repositories.
  • 1.5 Graphing Data – software (Excel, R, Python matplotlib) and best-practice labelling.
  • 1.6 Experimental Design – iterative process, pilot studies, peer feedback.