Reproducibility

Why Reproducibility

• YOU are responsible for your work

• Replication is often times impossible

• Validation of your methods

• Validation of your data

• So discrepancies can be identified ... and remedied (by you) or your vindication.

• No one is immune from errors

  A former manager of mine said "The only people who don't make mistakes are those who don't do anything"

For Example

Your Work

For Example

Someone else's Work

For Example

Both Charts

For Example

Your chart, with documentation

Examples of faulty data analysis

Colbert Report

What To Document

Data Source, including date

Includes the analytic data, not necessarily the raw data

Software Used, including version

Documentation of code and data

Challenge Results

Whether you do, or not, someone else looking at your data will.

• Challenge your steps

  • Your initial question
  • The data source
  • The processing you did
  • Your analysis
  • Your conclusions

Challenge Results (cont)

• Challenge measures of Uncertainty

• Challenge choices of terms to include in analysis

• Think about alternative analysis