Micro Statistics Tutorial 01: Lessons from Dave, the forecasting pig

Most (not all) statistics is about prediction. Forecasting is about prediction of future events (usually, but not always, in advance of those events). For example, Carrie Smith Cox, President of Schering-Plough, dumped $28 million worth of stock before anyone knew about negative studies, altered endpoints or anything at all about the drug Vytorin (ezetimibe/simvastatin-combination). That is an example of forecasting. We might however want to predict events in one group of people, knowing what happened in another group.

Here is where we introduce Dave, the Forecasting Pig (Reuters 31 Jan 2008). Dave lives in Ohio. He opines (or oswines) on US economic status, and is a key statistical tool used by the Ohio treasurer's office. Dave decides between a trough of sugar or one of sawdust to gauge the economy's future. Sadly, better methodologies are available (ask Carrie Smith Cox).

Tutorial take home message:
The ingredients of good statistics:

1. A decent, honest, well described set of data
2. Proper definition of terms, specified in advance
3. A clear, well framed and unambiguous question (or problem), specified in advance
4. A plan for examining those data, specified in advance
5. Honest intent

Example of a well framed unambiguous question:
Have you had the measles?
If so, how many?
(Armstrong Ward)

See here for Collated Micro-Statistics Tutorials

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