Module 1 - Lesson 7: Hypothesis testing, and risks for policy from poor data

[turukawa]

ETHICS

Assess the risks of population exclusion and hazardous externalities on data quality.

Statistics are not collected in a vacuum. They can inform and lead to policy implementation.
Consider the implications for policy when data samples are not representative or exclude the most at risk.
Examples: marginalised groups marginalisation reinforced through policy (cf African American pain management).

CURATION

Employ and assess methods for data collection and sampling.

Producing new data requires a protocol and a clear description of process and assumptions.
Any bias or oversampling to represent specific groups must be documented, and must serve research objectives.

ANALYSIS

Validate statistical data through hypotheses testing and error probabilities.

Hypothesis testing frameworks, skew and p-values, error probabilities.
p-thresholds (& 0.5), and significance levels.

PRESENTATION

Present p-values, confidence and significance to support analysis.

---

CASE STUDY

Example where risk of exclusion leads to poor policy. E.g. testing for food safety (bias by geography) – dataset has geospatial data. Build hypothesis testing into case study.