Scientific inquiry: from hypothesis to communication

A rigorous scientific investigation follows a connected process. It starts with an investigable question and a reasoned hypothesis. The investigation must be planned to be valid (actually testing what it claims to test) and reproducible (detailed enough that someone else could repeat it and get similar results), with careful control of variables and sources of error, using equipment precisely enough to generate useful, replicable data. Once collected, data is organised into appropriate representations (tables, graphs, descriptive statistics) to reveal patterns, trends and anomalies. Conclusions should be built from evidence-based arguments — explicitly acknowledging assumptions, uncertainty and any conflicting evidence — and then communicated clearly, in a form and language suited to the intended audience and purpose.

Key terms

Valid investigation:

One that actually tests what it claims to test, with controlled variables.

Reproducible:

Detailed enough that someone else could repeat the investigation and get similar results.

Questions

1. 1. A valid investigation:

   • Actually tests what it claims to test
   • Never needs to control any variables
   • Always produces a random, meaningless result
   • Has no connection to its original question

2. 2. A reproducible investigation is one that:

   • Someone else could repeat and get similar results
   • Can never be repeated by anyone else
   • Has no clear method described
   • Always produces a completely different result each time

3. 3. Data should be organised into:

   • Appropriate representations like tables and graphs
   • A completely random, unordered list
   • Nothing at all, just raw numbers with no structure
   • Only spoken descriptions, never visual displays

4. 4. A conclusion should be built from:

   • Evidence-based arguments
   • Pure guesswork with no evidence
   • Only the researcher's personal opinion
   • Whatever result was expected in advance

5. 5. Good scientific communication considers:

   • The intended audience and purpose
   • Nothing about who will read it
   • Only technical jargon, regardless of audience
   • Only the researcher's own preferences

6. 6. A rigorous investigation should acknowledge:

   • Assumptions, uncertainty and any conflicting evidence
   • Nothing beyond a single confident conclusion
   • Only evidence that supports the expected result
   • That every finding is completely certain

7. 7. Recording data from multiple plants, rather than just one, helps ensure:

   • More replicable, reliable data
   • A less trustworthy result
   • No difference to the investigation at all
   • That the investigation cannot be analysed

8. 8. Why is controlling variables important for an investigation to be considered "valid"?

   • It ensures that any observed effect can be confidently attributed to the variable actually being tested, not something else
   • Controlling variables has no bearing on whether an investigation is valid
   • A valid investigation never needs to control for any possible variables
   • Uncontrolled variables always improve the validity of an investigation

9. 9. Why might a detailed, precise method description be essential for an investigation to be reproducible?

   • Others need enough specific detail to repeat the exact same procedure and fairly compare results
   • Vague, undocumented methods are just as reproducible as detailed ones
   • Reproducibility has no connection to how a method is documented
   • Detailed method descriptions actually make an investigation harder to repeat

10. 10. Why might choosing an appropriate graph or table for your data be considered part of rigorous scientific practice, not just a presentation choice?

    • The right representation makes patterns, trends and anomalies genuinely easier to identify and analyse accurately
    • The choice of data representation never actually affects how well patterns can be identified
    • Any representation works equally well for any type of data with no meaningful difference
    • Data representation is purely a stylistic choice with no connection to rigorous analysis

11. 11. Why should a scientific conclusion explicitly acknowledge assumptions and any conflicting evidence, rather than presenting only the supporting evidence?

    • A transparent, honest account of all relevant evidence allows others to fairly judge the strength of the conclusion
    • Only supporting evidence should ever be included in a scientific report
    • Acknowledging assumptions and conflicting evidence always makes a conclusion less credible
    • Conflicting evidence should always be hidden to make a report seem more confident

12. 12. Why might communicating the exact same scientific findings differently for a scientific journal versus a school newsletter still be considered good scientific practice?

    • Adapting language and detail for the audience while keeping the substance accurate helps the findings actually be understood by their intended readers
    • Good scientific practice always requires using identical technical language for every possible audience
    • Adapting communication for different audiences is a sign of scientific dishonesty
    • Audience has no bearing on how scientific findings should be communicated

13. 13. Why might identifying and controlling for likely sources of error be an essential step in the planning stage of an investigation, not just something to consider afterward?

    • Planning for error sources in advance helps design a more reliable investigation from the start, rather than trying to explain away flawed results later
    • Sources of error only ever need to be considered after all data has already been collected
    • Planning for error sources in advance has no real effect on the quality of an investigation
    • A well-designed investigation never actually needs to consider any potential sources of error

14. 14. Why might a student's investigation into fertiliser and plant height be considered scientifically weaker if it tested only one plant per fertiliser amount, compared to testing several?

    • A single plant's result could be affected by chance individual variation, while multiple plants give a more reliable, replicable picture of the true effect
    • Testing only one plant per condition always produces exactly the same reliability as testing several
    • Individual variation between plants never actually affects investigation results
    • Sample size has no bearing on how confidently a conclusion can be drawn from data

15. 15. Why might a scientist need to critically evaluate not just their own investigation's validity, but also whether their final conclusions and claims are properly supported by the evidence gathered?

    • Even a well-designed investigation could still lead to conclusions that overstate what the actual evidence supports, so this final check matters
    • A valid investigation design always automatically guarantees a perfectly supported conclusion
    • Evaluating your own conclusions against your evidence is an unnecessary, redundant step
    • Investigation validity and conclusion validity are always exactly the same consideration

16. 16. Why might the full inquiry process (question, hypothesis, valid investigation, data analysis, honest conclusion, clear communication) be considered a connected cycle, rather than separate, unrelated steps?

    • Weakness at any single stage (like an invalid method or unclear communication) can undermine the value of the entire investigation, regardless of how strong other stages were
    • Each stage of the inquiry process is completely independent with no bearing on any of the others
    • Only the final conclusion stage actually matters for a scientific investigation's overall quality
    • The various stages of scientific inquiry have no meaningful connection to each other

17. 17. A student's investigation finds no significant relationship between fertiliser amount and plant height, contradicting their original hypothesis. What is the most scientifically appropriate next step?

    • Report the actual result honestly, including that it did not support the original hypothesis, rather than adjusting the conclusion to match expectations
    • Discard the data and repeat the experiment until a result matching the hypothesis is found
    • Report the hypothesis as confirmed regardless of what the data actually showed
    • Avoid reporting the investigation at all since it did not produce the expected result

18. 18. Why might a well-designed investigation still lead to a weak or unsupported conclusion if the final write-up doesn't clearly connect the evidence to the claims being made?

    • Even strong data needs to be explicitly and logically linked to the conclusion for the argument to be genuinely convincing and well-supported
    • A well-designed investigation always automatically produces a well-supported written conclusion with no further effort needed
    • The connection between evidence and conclusion has no bearing on how convincing a scientific report is
    • Strong data alone is always sufficient regardless of how the final conclusion is actually argued or written

19. 19. Why might two independent research teams investigating the same question, using different but equally valid methods, sometimes arrive at slightly different conclusions?

    • Different valid methods can have different strengths, sensitivities and limitations, which can lead to genuinely different but still legitimate findings
    • Two valid scientific methods must always produce identical results with zero variation
    • Any difference in results between two studies always means one of them made a significant error
    • Method choice has no bearing whatsoever on the specific results a study produces

20. 20. Why might the full scientific inquiry cycle be considered iterative — meaning a completed investigation often leads to new questions rather than a final, complete answer?

    • Findings often raise new questions or reveal further complexity, prompting additional investigable questions and hypotheses
    • A single investigation should always produce one complete, final, unquestionable answer to a topic
    • The inquiry cycle only ever runs once per scientific question, with no possibility of follow-up investigation
    • New questions arising from research findings indicates the original investigation must have failed

21. 21. Understanding the full process of scientific inquiry from hypothesis to communication mainly helps you to:

    • Design, conduct and report a rigorous, honest scientific investigation from start to finish
    • Focus only on forming a hypothesis, without conducting or reporting the actual investigation
    • Skip data analysis and go straight from question to conclusion
    • Report only the findings that best support the original hypothesis