Communication and scientific literacy: presenting data, descriptive and inferential statistics, evaluating reliability and validity, the critical evaluation of research, scientific ethics and integrity, and the structure of a scientific report.

An SQA Advanced Higher Biology answer on communication and scientific literacy, covering the presentation of data, descriptive and inferential statistics, the evaluation of reliability and validity, the critical evaluation of biological research, scientific ethics and integrity in reporting, and the structure of a scientific report.

Generated by Claude Opus 4.811 min answerUpdated 2026-06-14

Reviewed by: AI editorial process; not yet individually human-reviewed

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Quick answer

Data are presented in the right form: line graphs for continuous variables, bar graphs for categories, scatter plots for relationships, all with labels, units, scales and error bars. Descriptive statistics (mean, median, mode, range, standard deviation) summarise data, while inferential statistics test whether differences or relationships are likely to be real rather than due to chance; error bars indicate variability. Reliability is consistency on repetition; validity is whether the study tests what it claims, which needs confounding variables controlled. Research is evaluated critically by examining design, sample size, controls and whether conclusions follow from results, distinguishing results from interpretation. Integrity means honest recording, no fabrication or plagiarism, and proper referencing. A scientific report follows a standard structure of title, abstract, introduction, methods, results, discussion and references.

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What this key area is asking
Presenting data
Descriptive and inferential statistics
Reliability, validity and critical evaluation
Integrity and the scientific report
Examples in context
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What this key area is asking

The SQA wants you to handle the end of the scientific process: presenting data clearly, using descriptive and inferential statistics, evaluating reliability and validity, critically appraising published research, applying integrity in reporting, and structuring a scientific report. These are the skills the project is marked on.

Presenting data

Descriptive and inferential statistics

Error bars on a graph show the variability or uncertainty in each mean. Non-overlapping error bars suggest a real difference; substantial overlap suggests the difference could be due to chance, and a statistical test is needed.

Reliability, validity and critical evaluation

Integrity and the scientific report

Examples in context

Example 1. Reading a graph with error bars. Two treatment means look different, but their error bars overlap heavily, so the report correctly states that a statistical test is needed before claiming a real effect. The example shows error bars guiding a cautious, valid conclusion.

Example 2. A retracted study from poor practice. A paper with fabricated data is retracted once others cannot reproduce it. The example shows why integrity and reproducibility matter and how the scientific community corrects itself.

Try this

Q1. Name the statistic that describes the spread of a set of data. [1 mark]

Cue. The standard deviation (or the range).

Q2. Explain the difference between a result and a conclusion. [2 marks]

Cue. A result is the data measured; a conclusion is the interpretation of what the data mean.

Exam-style practice questions

Practice questions written in the style of SQA exam questions on this dot point, with worked answer explainers. The year tag is the paper they imitate, not the source.

SQA AH style4 marksExplain what error bars show on a graph and how they help in judging whether two means differ.

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A 4-mark answer needs what error bars represent and how they are used.

Error bars represent the variability or uncertainty in a set of measurements, for example the standard deviation or a confidence interval around each mean.

When the error bars of two means do not overlap, it suggests the difference between them is unlikely to be due to chance alone, giving more confidence that there is a real difference.

When the error bars overlap substantially, the apparent difference may be due to natural variation, and a statistical test is needed before concluding there is a real difference.

Markers reward (1) error bars show variability or uncertainty, (2) non-overlapping bars suggest a real difference, and (3) and (4) overlapping bars suggest the difference may be due to chance so a test is needed.

SQA AH style3 marksCritically evaluate a study that concludes a supplement improves memory from a survey of people who choose to take it.

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A 3-mark answer needs the design weakness, the confounding issue and the correlation point.

The study is observational and self-selected: people who choose to take the supplement may differ from those who do not, for example in diet or lifestyle, so these are confounding variables.

Because nothing is controlled or randomised, any link between the supplement and memory is a correlation and does not prove that the supplement causes the improvement.

A controlled, randomised, blinded experiment with a placebo would be needed to test causation.

Markers reward (1) self-selected observational design with confounding variables, (2) correlation does not prove causation, and (3) a controlled randomised trial is needed.

Related dot points

Sources & how we know this

SQA Advanced Higher Biology Course Specification — SQA (2019)