Analysing data: patterns, trends & conclusions

Once an investigation's data is collected, it needs to be organised and analysed to be useful. Tables and graphs help represent numerical data so patterns and trends become visible — like a line graph showing how a plant's height increases over time. When analysing data, look for patterns (a consistent trend), and also check for anomalies — results that don't fit the pattern, and might indicate an error or something interesting worth investigating further. Drawing a conclusion means using the evidence, patterns and trends found to answer the original question, while also considering possible sources of error or alternative explanations.

Key terms

Trend:

A general pattern or direction shown by data over time or across conditions.

Anomaly:

A result that doesn't fit the expected pattern, possibly due to an error or an interesting exception.

Conclusion:

A statement, based on evidence, that answers the original investigation question.

Questions

1. 1. Tables and graphs help you:

   • Hide data
   • See patterns in data
   • Avoid analysing data
   • Remove all numbers

2. 2. A trend is:

   • A single random number
   • A general pattern or direction in data
   • Always an error
   • Irrelevant to science

3. 3. An anomaly is:

   • A normal expected result
   • A result that doesn't fit the expected pattern
   • Always correct
   • The average result

4. 4. A conclusion should be based on:

   • Guessing
   • Evidence from the investigation
   • Random opinions
   • Nothing at all

5. 5. A line graph is especially useful for showing:

   • Colours
   • Change over time
   • Nothing useful
   • Random data only

6. 6. If a data point doesn't match the overall trend, it might indicate:

   • Nothing worth checking
   • A possible error or something worth investigating
   • That all the data is wrong
   • That the experiment failed completely

7. 7. Analysing data means:

   • Ignoring the results
   • Looking for patterns and meaning in the results
   • Deleting the data
   • Repeating the question only

8. 8. A graph of plant height over 4 weeks shows a steady increase except for one lower point in week 3. This point is likely:

   • The most important data point, ignore the rest
   • An anomaly worth investigating further
   • Proof the whole experiment failed
   • Impossible to interpret

9. 9. When drawing a conclusion, you should consider:

   • Only the parts of data you like
   • The evidence, patterns and possible sources of error
   • Nothing except your original guess
   • Random unrelated facts

10. 10. Which graph type would best show how temperature changes over 24 hours?

    • A pie chart
    • A line graph
    • A single number
    • No graph is useful

11. 11. If most data points support a clear trend, but one doesn't, a careful analysis would:

    • Ignore the trend entirely
    • Report the trend and also note the anomaly
    • Delete all the data
    • Assume the whole experiment is invalid

12. 12. A possible source of error in a measurement-based investigation could include:

    • Reading a scale incorrectly
    • Perfectly accurate results only
    • No possible errors ever
    • Only computer errors

13. 13. Why is it important to consider alternative explanations before finalising a conclusion?

    • To make the investigation take longer
    • To ensure the conclusion is well-supported and not jumping to unjustified assumptions
    • Alternative explanations are never useful
    • Conclusions should ignore alternatives entirely

14. 14. A pattern that consistently repeats across multiple trials is generally considered:

    • Unreliable
    • More reliable evidence for a real trend
    • Definitely an error
    • Irrelevant

15. 15. An investigation finds a clear trend in 9 out of 10 trials, with one trial showing a very different result. What is the most scientific next step?

    • Ignore the anomaly completely and never mention it
    • Investigate whether the anomaly was due to an error or reflects a genuine exception worth exploring
    • Delete the 9 trials that support the trend
    • Assume the experiment is entirely useless

16. 16. Why might a scientist repeat an experiment multiple times rather than relying on a single trial?

    • Repeated trials are unnecessary
    • Repetition helps distinguish a genuine trend from random variation or a one-off anomaly
    • Single trials are always sufficient
    • More trials always distort the truth

17. 17. A student concludes "Fertiliser definitely makes all plants grow faster" from a small, 3-plant experiment. What is a reasonable critique?

    • It's a perfectly certain conclusion
    • The sample size is small, so the conclusion may be an overgeneralisation needing more evidence
    • No conclusion should ever be drawn from data
    • The conclusion is unrelated to the data

18. 18. Which best distinguishes a strong data-based conclusion from a weak one?

    • A strong conclusion directly reflects the evidence, considers error sources, and doesn't overreach beyond what the data shows
    • A strong conclusion is always the longest one
    • Weak conclusions use more graphs
    • Strength has nothing to do with evidence

19. 19. Why is identifying possible sources of error important even when a trend seems clear?

    • Errors are impossible in careful investigations
    • It helps assess how confident to be in the conclusion, and whether the trend is genuinely reliable
    • Considering errors weakens all conclusions unnecessarily
    • Only anomalies matter, not general error sources

20. 20. Understanding how to analyse data for patterns, trends and anomalies mainly helps a scientist:

    • Ignore inconvenient results
    • Draw well-supported, evidence-based conclusions rather than jumping to assumptions
    • Avoid ever questioning their own results
    • Skip the need for a hypothesis