The Data Errors we Make by Sean Taylor at Big Data Spain 2017

Where statistical errors come from, how they cause us to make bad decisions, and what to do about it.

https://www.bigdataspain.org/2017/talk/the-data-errors-we-make

Big Data Spain 2017
16th - 17th November Kinépolis Madrid

Transcript

2. The Data Errors We Make Sean J Taylor Core Data Science Team Facebook
3. About Me • 5 years at Facebook as a Research Scientist • PhD in Information Systems from New York University • Research Interests: • Field Experiments • Forecasting • Sports and sports fans https://facebook.github.io/prophet/
4. Strategic Decisions Micro-decisions at Scale
6. Data Algorithm Human
 Choices Estimate Decision Outcome Truth statistical 
 error practical 
 error Optimal Decision Optimal
 Outcome
7. Simplest Error Model
8. H0: You are not pregnant. H1: You are pregnant.
9. H0 is True Product is Bad H1 is True Product is Good Accept Null Hypothesis (Don’t ship product) Right decision Type II Error (wrong decision) Reject Null Hypothesis (Ship Product) Type I Error (wrong decision) Right decision
10. Receiver Operating Characteristic (ROC) Curve tells us Type I and II error rates Type I error rate (1 - Type II error rate)
11. Outline 1. Refinements to the Type I/II error model 2. A simple causal model of how we make errors 3. What we can effectively do about errors
12. Refinements
13. Refinement 1:
 Assign Costs to Errors H0 is True Product is Bad H1 is True Product is Good Accept Null Hypothesis (Don’t ship product) Right decision Type II Error (wrong decision) Reject Null Hypothesis (Ship Product) Type I Error (wrong decision) Right decision
14. Refinement 1:
 Assign Costs to Errors H0 is True Product is Bad H1 is True Product is Good Accept Null Hypothesis (Don’t ship product) 0 -100 Reject Null Hypothesis (Ship Product) -200 +100
15. Example: 
 Expected value of a product launch P(Type I) is 1% and P(Type II) is 20% P(good) * (100 * .80 + -100 * .2) + (1 - P(good)) * (-200 * .01 + 0 * .99) = (.5 * 60) + (.5 * -2) = 30 - 1 = 29
16. Allowing more Type I errors lowers Type II rate. Optimal choice depends on payoffs and P(H1).
17. P(Type I) is 5% and P(Type II) is 7% P(good) * (100 * .93 + -100 *.07) + (1 - P(good)) * (-200 * .05 + 0 * .95) = (.5 * 86) + (.5 * -10) = 43 - 5 = 38 > 29 Example 2: 
 Expected value of a product launch
18. Refinement 2: Opportunity Cost Key Idea: If we devote resources to minimizing Type I and II errors for one problem, we will have fewer resources for other problems. • Few organizations makes a single decision, we usually make many of them. • Acquiring more data, investing more time into problems has diminishing marginal returns.
19. Examples of Constraints • Sample size for online experiments • Gathering more data • Analyst time
20. Refinement 3: Mosteller’s Type III Errors 
 Type III error: “correctly rejecting the null hypothesis for the wrong reason” -- Frederick Mosteller More clearly: The process you used worked this time, but is unlikely to continue working in the future.
21. Good Process vs. Good Outcome Good Outcome Bad Outcome Good Process Deserved Success Bad Break Bad Process Dumb Luck Poetic Justice
22. Refinement 4: Kimball’s Type III Errors 
 Type III error: “the error committed by giving the right answer to the wrong problem” -- Allyn W. Kimball
25. Why we make errors
26. Data Algorithm Human
 Choices Estimate
27. Cause 1: Data • Inadequate data • Non-representative data • Measuring the wrong thing
28. made data designed to be adequate found data adequate if we are fortunate
29. Non-representative data
30. 2014 World Cup First Facebook Check-ins in Brazil from non-Brazilian users
31. Bias? 2014 World Cup Check-ins by Country
32. Measuring the wrong thing
35. Common Pattern • High volume of of cheap, easy to measure “surrogate” 
 (e.g. steps, clicks) • Surrogate is correlated with true measurement of interest (e.g. overall health, purchase intention) • key question: sign and magnitude of “interpretation bias”
36. Cause 2: Algorithms • The model/procedure we choose primarily concerns what side of the bias-variance tradeoff we'd like to be on. • Common mistakes are: • Using a model that’s too complex for the data. • Focusing too much on algorithms instead of gathering the right data or correctness.
37. Optimizing models Reducing bias • Choose a more flexible model. Reducing variance • Choosing a less flexible model. • Get more data.
38. Tree Induction vs. Logistic Regression: A Learning-Curve Analysis
 Perlich et al. (2003) • logistic regression is better for smaller training sets and tree induction for larger data sets • logistic regression is usually better when the signal-to- noise ratio is lower
39. Cause 3: Human choices Many analysts, one dataset: Making transparent how variations in analytical choices affect results
 (Silberzahn et al. 2017) • 29 teams involving 61 analysts used the same dataset to address the same research question • Are soccer ⚽ referees are more likely to give red cards to dark skin toned players than light skin toned players?
40. • effect sizes ranged from 0.89 to 2.93 in odds ratio units • 20 teams (69%) found a statistically significant positive effect • 9 teams (31%) observed a nonsignificant relationship
41. Overconfidence
42. Incentives
43. Ways Forward • prevent errors • opinionated analysis development • test driven data analysis • be honest about uncertainty • estimate uncertainty using the bootstrap
44. Opinionated Analysis Development
 (by Hilary Parker)
45. Test-Driven Data Analysis
48. Estimating Uncertainty
49. No algorithm in Scikit Learn 
 will estimate uncertainty.
50. The Bootstrap R1 All Your Data R2 … R500 Generate random sub-samples s1 s2 s500 Compute statistics or estimate model parameters … } 0.0 2.5 5.0 7.5 -2 -1 0 1 2 Statistic Count Get a distribution over statistic of interest (usually the prediction) - take mean - CIs == 95% quantiles - SEs == standard deviation
51. Summary Think about errors! • What kind of errors are we making? • Where did the come from? Prevent errors! • Use a reasonable and reproducible process. • Test your analysis as you test your code. Estimate uncertainty! • Models that estimate uncertainty are more useful than those that don’t. • They facilitate better learning and experimentation.