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than in the false allegations, which is in agreement with the Undeutsch hypothesis. In a better controlled eld study where the ground truth was established, Michael Lamb and his colleagues analysed the statements of 98 alleged victims of child sexual abuse (aged 4 to 12 years) (Lamb, Sternberg, Esplin, Hershkowitz, Orbach, & Hovav, 1997b). They included only cases where there was evidence of actual physical contact between a known accused and the child and an element of corroboration was present. Using these selection criteria meant that many cases needed to be disregarded, as the initial sample consisted of 1,187 interviews. If a criterion was absent in the statement, it received a score of 0; if it was present, it received a score of 1. They found fewer signi cant differences than, for example, Boychuk or Esplin et al., partly because 14 rather than 19 criteria CBCA criteria were analysed. However, again, all differences were in the expected direction: the criteria were more often present in the plausible group than in the implausible group. They also calculated total CBCA scores, which could vary between 0 and 14. Signi cantly more criteria were present in the plausible group (6.74) than in the implausible group (4.85). This difference, however, is much smaller than the difference found by Esplin and colleagues.11 Summary In ve of the six CBCA eld studies the ground truth has not been satisfactorily established. This means that caution is required when interpreting the ndings of these ve studies because they cannot really be trusted. However, a trend emerges when all of the eld studies are taken together. The only reliable eld study published to date showed

Aserinsky, E., & Kleitman, N. (1953). Regularly occurring periods of eye motility, and concomitant phenomena, during sleep. Science, 118(3062), 273 274.

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end of the data preparation phase, one should be very familiar with the data and should already start identifying aspects of the data relating to the problem being solved The steps performed in preparing the data should be documented A data set ready for analysis should have been prepared 3 Implementation of the analysis: There are three primary tasks that relate to any data analysis or data mining project: summarizing the data, nding hidden relationships, and making predictions When implementing the analysis one should select appropriate methods that match the task, the data, and the objectives of the project Available methods include graphing the data, summarizing the data in tables, descriptive statistics, inferential statistics, correlation analysis, grouping methods, and mathematical models Graphs, summary tables, and descriptive statistics are essential for summarizing data.

You can fix the Localized Data AntiPattern by changing your data architecture. As an example, let s go back to the case of a single Web application being moved to a multiWeb-server installation. Originally, all of the customer data was on one Web server, stored in files. While you can copy it, as pictured in Figure 1.3, if one server handles the same customers as another, you may end up with data that is out of sync. You might even corrupt your data if two servlets from different servers perform mutually exclusive operations on the customer data before their files are synchronized.

You have embedded code in many servlets that applies the same pre- or postprocessing to the requests that are serviced. Place the common functionality into a filter and control the applicability and order of the preor postprocessing with the deployment descriptor.

Where general statements about populations are needed, inferential statistics should be used to understand the statistical signi cance of the summaries Where a method is being used for grouping or prediction, appropriate methods should be selected that match the objectives of the projects and the available data These methods should be ned-tuned, adjusting the parameters within a controlled experiment When assessing the quality of a prediction model, a separate test and training set should be used When presenting the results of the analysis, any transformed data should be presented in its original form Appropriate methods for explaining and qualifying the results should be developed when needed Where an analysis is based on multiple models, speci c model selection criteria and/or composite models should be developed 4 Deployment: A plan should be set up to deliver the results of the analysis to the already identi ed consumer.

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