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Sampling and Statistics

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Wage and Hour Law

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Abstract

In this chapter, I address several issues related to statistical sampling and statistical analysis when used as evidence in wage and hour litigation. Sampling and statistical analysis are commonly used in a variety of legal contexts including antitrust, employment discrimination, toxic torts, and voting rights cases. Properly designed and executed statistical analysis is generally considered admissible in litigation under the Federal Rules of Evidence as most sampling and analysis methods meet the “scientific knowledge” requirement in Daubert v. Merrell Dow Pharmaceuticals, a case which guides the admissibility of expert evidence in litigation. This chapter is focused on aspects of sampling and statistical analysis that are most frequently applied to address wage and hour disputes. This typically includes descriptive statistics to summarize data collected from a sample or estimates of population characteristics (“parameters”) based on data from a sample. Disputes over sampling and statistical analysis often play a major role in a court’s decisions to certify a class or determine liability. Disputes tend to be unrelated to the accuracy of the calculations but rather the reliability of the underlying data, the representativeness of the sample from which the data were collected, or the proper interpretation of statistical analysis results. Topics covered included statistical sampling, non-response bias, extrapolation and confidence intervals, variability, data quality, and damage calculations.

The original version of this chapter was revised. A correction to this chapter can be found at https://doi.org/10.1007/978-3-319-74612-8_10

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Notes

  1. 1.

    see, generally, Gastwirth (2000), DeGroot et al. (1986), and Fienberg (1989).

  2. 2.

    Kaye and Freedman (2011).

  3. 3.

    For simplicity, the term “class action” is used for the remainder of this chapter to refer to both types of multi-plaintiff actions. See Chap. 1 for more detail.

  4. 4.

    Kaye and Freedman (2011).

  5. 5.

    See Diamond (2011); Babbie (1990).

  6. 6.

    See, e.g., Thompson (2012).

  7. 7.

    Diamond (2011).

  8. 8.

    Diamond (2011).

  9. 9.

    Diamond (2011).

  10. 10.

    This does not mean that former and current employees are similar for purposes of addressing class certification decisions. It means that the pattern of variability within current employees is likely similar to what would be found for former employees.

  11. 11.

    See Diamond (2011); Kaye and Freedman (2011); Babbie (1990).

  12. 12.

    Kaye and Freedman (2011).

  13. 13.

    See Thompson (2012) for further discussion of non-probability samples.

  14. 14.

    See Babbie (1990).

  15. 15.

    Diamond (2011). It is common in job analysis to exclude employees from the sample if they have not been the position for a period of time long enough to learn the job fully or who are known to not be performing the job adequately (e.g., undergoing disciplinary action).

  16. 16.

    Random number generators are available in most statistical analysis software, including Microsoft Excel.

  17. 17.

    Diamond (2011).

  18. 18.

    Kaye and Freedman (2011).

  19. 19.

    Larger samples and samples from homogeneous populations are less susceptible to sampling error (Kaye & Freedman, 2011; Babbie, 1990).

  20. 20.

    Diamond (2011); Babbie (1990).

  21. 21.

    Finberg and Thoreen (2007).

  22. 22.

    Thompson (2012).

  23. 23.

    Kaye and Freedman (2011).

  24. 24.

    In some situations, samples are supplemented at a later time; however a sample size still must be specified to select the initial sample.

  25. 25.

    Surprisingly, population size usually has little impact on the degree of precision of estimates or the required sample size (Kaye & Freedman, 2011).

  26. 26.

    Most textbooks on statistical analysis and sampling (e.g., Thompson, 2012) contain formulas and steps for calculating confidence intervals in a variety of scenarios.

  27. 27.

    Most experts apply a 95% confidence interval; however there may be some situations where a 90% or a 99% confidence interval is used (Kaye & Freedman, 2011).

  28. 28.

    As an example, the California Supreme Court in Duran v US Bank (described later in the chapter) found that a relative confidence interval of 43% (confidence interval/average) contained too much uncertainty and the analyses were not accepted.

  29. 29.

    Kaye and Freedman (2011).

  30. 30.

    When estimating a proportion, the maximum possible variability occurs when the estimate is 0.50. That is, 50% of the group fall into one category and 50% fall into the other category.

  31. 31.

    See, e.g., Brase and Brase (2011); Howell (2010).

  32. 32.

    Krosnick and Presser (2010); Stetz, Beaubien, Keeney & Lyons (2008).

  33. 33.

    Diamond (2011).

  34. 34.

    Diamond (2011).

  35. 35.

    Krosnick and Presser (2010).

  36. 36.

    Kaye and Freedman (2011); Babbie (1990).

  37. 37.

    Kaye and Freedman (2011).

  38. 38.

    Kaye and Freedman (2011); Howell (2010).

  39. 39.

    Howell (2010).

  40. 40.

    Diamond (2011).

  41. 41.

    Kaye and Freedman (2011).

  42. 42.

    Diamond (2011).

  43. 43.

    Kaye and Freedman (2011).

  44. 44.

    Kaye and Freedman (2011).

  45. 45.

    See, e.g., Thompson (2012); Howell (2010); Witte and Witte (2010).

  46. 46.

    Kaye and Freedman (2011).

  47. 47.

    Some recent strategies have been proposed for establishing objective thresholds including rules of thumb for the coefficient of variation (Murphy, 2014) and repeated measure strategies (Hanvey, 2014). No strategy has yet been widely accepted and applied.

  48. 48.

    American Statistical Association (2016).

  49. 49.

    Allen et al. (2011).

  50. 50.

    Kaye and Freedman (2011).

  51. 51.

    Wheelan (2013) (p. 111).

  52. 52.

    Kaye and Freedman (2011); Allen et al. (2011).

  53. 53.

    I use the term “statistician” in this chapter to refer to any person performing statistical analysis, regardless of their educational or professional background.

  54. 54.

    See Kaye and Freedman (2011).

  55. 55.

    Allen et al. (2011).

  56. 56.

    Allen et al. (2011).

  57. 57.

    Allen et al. (2011).

  58. 58.

    In many organizations, an IT or HRIS employee will be the most knowledgeable about the details of the data.

  59. 59.

    Allen et al. (2011).

  60. 60.

    “[B]roadly speaking, the law tolerates more uncertainty with respect to damages than to the existence of liability” Duran v US Bank (p. 38).

  61. 61.

    See Duran v. US Bank; Bruckman v. Parliament Escrow Corp. (1987).

  62. 62.

    Duran v. US Bank (p. 2).

  63. 63.

    Duran v. US Bank (p. 14).

  64. 64.

    The PPE included protection against knife cuts which both parties agreed was compensable.

  65. 65.

    Some employees were compensated between 4 and 8 min for donning and doffing PPE. This time was removed as part of the calculation.

  66. 66.

    Tyson Foods, Inc. v Bouaphakeo et al., (p. 2).

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  1. Berkeley Research Group, Emeryville, CA, USA

    Chester Hanvey

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Hanvey, C. (2018). Sampling and Statistics. In: Wage and Hour Law. Springer, Cham. https://doi.org/10.1007/978-3-319-74612-8_8

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