Haldna presentation

professor of Psychology Rand R.Wilcox, University of Southern California

Written by Marina Haldna

To introduce and compare different statistical methods

Introduction Methods Results Conclusion

Typical situatsions : before-after two places with parallel measurements

Every pair of sampled data has a important information to make a correct conclusion

Data are missing at random. The reason why a data point is missing is

not related to its actual value

Simple strategy is to compute a confidence interval using a normal or t-distribution, this approach may be unsatisfactory (Liang et al. 2008)

Even if there are non-missing data, low power arise when sampling from a heavy-tiled distribution (Wilcox, 2005)

Median 20% trimmed mean Difference between the marginal trimmed

means

M1(means) M2(trimmed means) M3 –bootstrap method with trimmed means M4-medians

In terms of efficiency, the median generally performs better than 20% trimmed mean (Wilcox,2006)

To check the properties of the methods, one sample of correlated and an other non-correlated data from bivariate distribution were used

Simulations were repeated 3000 times, sample size was taken 30.

Two different cases for missing dates: 5 from one and 5 from an other group10 from one and 0 from an other group

In terms of Type I errors The method for comparing means can be

unsatisfactory Percentile bootstrap method with 20%

trimmed means performed well

In terms of power the M2 and M3 are more satisfactory

Statistics means never having to say you are certain

Statistics is the art of never having to say you are wrong

(http://www.btinternet.com/~se16/hgb/statjoke.htm)