Module 1 - Lesson 5: Expected statistical outcomes using distributions, and issues for analysis

[turukawa]

ETHICS

Recognise issues in analysing and exploring data for analysis.

Importance of prepublication on bias; will find correlation give law of large numbers.
Example: Abortion and crime.

CURATION

Infer interpolated data values using other data as input.

This is not necessarily “danger”, e.g. Net = Gross – Other, but the temptation could be to “create”
Note, this is also a data normalisation step (e.g. convert Y/N to True/False).
Always publish workings and definitions.

ANALYSIS

Assess expected statistical outcomes using geometric, binomial, and empirical distributions.

As above, using computational means to assess distributions.
Include simulations and case study.

PRESENTATION

Construct multiple plots, of different plot types, on a single set of axes.

Showing different data series, generated in different ways, and presented in different formats, on a single set of axes.
E.g. dot plots + histograms + line chart.

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CASE STUDY

Vaccination coverage for herd immunity. E.g. Measles vaccination, and impact of ani-vaccination.