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Summary statistics and confirmation

After the data has been Data Analysis - Clean and map data, we need to confirm that we have data that is understood and useable.

Whenever possible, build checks and balances into the ingestion/cleaning process. Particularly around how we know things make sense. This is critical because if the data is wrong, or our understanding of it is wrong, then everything everything else will be wrong, and nothing else matters.

This is a truth in the analysis business: If somebody finds a wrong number in your analysis, nothing else can be trusted afterward

1. Data summary statistics Data Analysis - Raw data understanding and summary statistics

2. Compare the data to external resources Data Analysis - External data validation