SCIE1001 Chap.3 Using and Misusing Data

Using and Misusing Data

Using and Misusing Data connects structure, process and observation through measurement validity, selection and visualisation and correlation and causation.

The chapter is useful when the task is to audit a public graph from variable definition through conclusion, because each claim must identify both the biological or behavioural system and the evidence used to distinguish it.

Locate measurement validity first: name the relevant structure, population, scale or experimental condition.

A label is not enough; orient it relative to the neighbouring structures or comparison group that gives the label meaning.

Data and sampling

In SCIE1001, data and sampling belongs with measurement validity and selection and visualisation because students use it to audit a public graph from variable definition through conclusion.

A defensible use of data and sampling should define the term, connect it to the case evidence and test the conclusion through correlation and causation; repeating the phrase without that chain does not demonstrate understanding.

Data viz ethics

In SCIE1001, data viz ethics belongs with measurement validity and selection and visualisation because students use it to audit a public graph from variable definition through conclusion.

A defensible use of data viz ethics should define the term, connect it to the case evidence and test the conclusion through correlation and causation; repeating the phrase without that chain does not demonstrate understanding.

Then use selection and visualisation to describe the process linking starting condition to outcome.

Keep sequence and direction clear, and separate an observed association from a mechanism that has actually been tested.

Use correlation and causation as the discriminating observation.

Ask what pattern would support the explanation, what plausible alternative could produce a similar pattern and what additional measurement would separate them.

In the application — audit a public graph from variable definition through conclusion — move from observation to interpretation in explicit stages.

Report uncertainty and variation rather than treating a representative diagram, specimen or mean as if every case were identical.

Create an observation ledger for Using and Misusing Data: specimen, participant or system; orientation or experimental condition; feature observed; comparison; and inference. Keep measurement validity in the observation columns and reserve selection and visualisation for the explanatory step.

This prevents a diagram label or group difference from being reported as a mechanism without supporting evidence.

Use a contrast case to test correlation and causation. Change one anatomical relation, exposure, task condition or comparison group while holding the rest of the scenario stable.

Predict which observation should change if the proposed explanation is correct and which result would favour an alternative explanation. That prediction gives the next measurement a clear purpose.

When revising SCIE1001, alternate identification with explanation.

First identify the relevant feature or pattern without notes; then explain how it contributes to audit a public graph from variable definition through conclusion; finally state the uncertainty or boundary that remains.

This sequence exposes the difference between recognising a familiar image or term and using it to answer a new scientific question.

A complete Using and Misusing Data response should make the task visible before the detail: identify what must be decided, define the relevant terms, connect the evidence to selection and visualisation, and use correlation and causation to test the result.

The final sentence should answer the question actually asked rather than merely repeat the topic.

The controlling limit is specific: A large dataset does not repair biased measurement or an invalid comparison.

Keep that limit beside the worked example, because it separates a careful SCIE1001 answer from one that sounds confident but claims more than the task or evidence supports.

For revision, retrieve measurement validity, selection and visualisation and correlation and causation without notes, explain their relationship aloud, then complete a changed version of the application: audit a public graph from variable definition through conclusion.

Record the first point at which your reasoning fails and repair that move before attempting another case.

What this chapter covers

• 01

  measurement validity

• 02

  selection and visualisation

• 03

  correlation and causation

• 04

  Applying measurement validity

• 05

  Limits of selection and visualisation and correlation and causation

Worked example: Using and Misusing Data

Key terms

p-value, p-hacking and the correct interpretation of conditional probability

A p-value is the probability, assuming a specified null model, of data at least as extreme as those observed; p-hacking selectively analyses or reports results to obtain significance and does not turn that conditional probability into the probability that the hypothesis is true. In this chapter, use the concept when you audit a public graph from variable definition through conclusion.

reproducibility vs replicability vs robustness, and questionable research practices

Reproducibility obtains the same result from the same data and analysis, replicability tests the finding with new data, and robustness checks whether it survives reasonable analytical changes; questionable practices can undermine all three. In this chapter, use the concept when you audit a public graph from variable definition through conclusion.

feminist empiricism and standpoint theory / epistemic benefits of diversity

Feminist empiricism examines how bias can be corrected through stronger methods and critical communities, while standpoint theory argues that social position can reveal otherwise hidden relations; diversity can therefore improve collective inquiry. In this chapter, use the concept when you audit a public graph from variable definition through conclusion.

Using and Misusing Data FAQ

Exam move

Reconstruct the relationship among measurement validity, selection and visualisation and correlation and causation; complete the chapter application without notes; then test the result against this limit: A large dataset does not repair biased measurement or an invalid comparison.

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