Validating accuracy of web server statistics updating patches for esx host

10-May-2020 16:27

The findings of these two types of measurement errors have different implications.

For example, in a study comparing self-reported data of height and weight with direct measured data (Hart & Tomazic, 1999), it was found that subjects tend to over-report their height but under-report their weight.

Sometimes people "remember" events that never happened.

Thus the reliability of self-reported data is tenuous.

For example, several versions of IQ tests are found to be bias against non-Whites.

It means that blacks and Hispanics tend to receive lower scores regardless of their actual intelligence.

Another concern about such data centers on whether subjects are able to accurately recall past behaviors.

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In other words, the reported scores could be either above or below the actual scores (Salvucci, Walter, Conley, Fink, & Saba, 1997).However, these options may have limited applicability.For example, the user access log cannot track users who follow links to other websites.However, by examining church attendance records, Hadaway and Marlar (2005) concluded that the actual attendance was fewer than 22 percent.In his seminal work Everybody lies, Seth Stephens-Davidowitz (2017) found ample evidence to show that most people do not do what they say and do not say what they do.

In other words, the reported scores could be either above or below the actual scores (Salvucci, Walter, Conley, Fink, & Saba, 1997).

However, these options may have limited applicability.

For example, the user access log cannot track users who follow links to other websites.

However, by examining church attendance records, Hadaway and Marlar (2005) concluded that the actual attendance was fewer than 22 percent.

In his seminal work Everybody lies, Seth Stephens-Davidowitz (2017) found ample evidence to show that most people do not do what they say and do not say what they do.

For example, the first user may over-estimate his Internet activities by 10%, but the second user may under-estimate hers by 10%. However, over-estimation and under-estimation increases variability of the distribution.