Publication: The effect of dependence between observations on the proper interpretation of statistical evidence
All || By Area || By Year| Title | The effect of dependence between observations on the proper interpretation of statistical evidence | Authors/Editors* | Gel Y., W. Miao, J. L. Gastwirth. |
|---|---|
| Where published* | Law, Probability and Risk |
| How published* | Journal |
| Year* | 2009 |
| Volume | to appear |
| Number | |
| Pages | 18 |
| Publisher | |
| Keywords | |
| Link | |
| Abstract |
In a recent securities law case, the statistical methods used by the regulator in analyzing data on daily commissions and hypothetical profits from Initial Public Offerings (IPOs) assumed that the data on consecutive days were independent. Consecutive observations in most business and economic data, however, are positively correlated. While statistical articles demonstrate that this type of dependence affects the distribution of virtually all statistics, including nonparametric and goodness-of-fit tests, the magnitude of the effect may not be fully appreciated. For example in one comparison of commissions one broker received on days with an IPO to the days when no IPO was issued yielded a statistically significant p-value of 0.02, under the independence assumption. Accounting for serial correlation, the test actually had a non-significant p-value close to 0.09. Other examples of the effect of dependence include jury discrimination cases in locales where grand jurors can serve two consecutive terms as well as cases concerned with environmental pollution where measurements are spatially and temporally correlated. This paper describes the noticeable effect of violations of the independence assumption can have on statistical inferences. The methods for correcting some standard non-parametric tests for serial correlation are also discussed. |
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