Title: 10 Principles of Practical Statistical Reasoning - KDnuggets
Headings: 10 Principles of Practical Statistical Reasoning
10 Principles of Practical Statistical Reasoning
Practical Statistical Reasoning
1 — Data quality matters
2 — Criticise variation
3 — Select a sensible depth of analysis
4 — Understand data structure
5 — 4 phases of statistical analysis
6 — What’s the output?
7 — Appropriate analysis style
8 — Computational consideration is only sometimes an issue
9 — Design investigations well
10 — Purpose of investigation
Final Word
Related:

Content: The interpretation of results will vary based on the investigation design: Experiments = system under study is set up and controlled by the investigator. Clear-cut differences can be attributed to variables confidently. Observational studies = the investigator has no control over data collection other than monitoring data quality. True explanatory variables may be missing, hard to draw conclusions with confidence. Sample surveys = sample drawn from a population by methods (randomisation) under the control of the investigator. Conclusions can be drawn in confidence on the descriptive properties of the population however explanatory variables suffer as above. Controlled prospective studies = sample chosen by the investigator, explanatory variables measured and followed over time. Has some virtues of Experiments but in reality, it’s not possible to measure all explanatory variables. Controlled retrospective studies = existing datasets with appropriate handling of explanatory variables.