Wednesday, August 25, 2021 12pm to 1pm
About this Event
Missing values can be defined as the absence of information in instances. It brings harmful consequences since the absence of data reduces statistical power. The lost data can cause bias in the estimation of parameters. It can reduce the representativeness of the samples, and it may complicate the analysis of the study.
Handling missing data validly is an important, yet difficult and complex task. In real-world settings, missing data types can be mixed, thus are impossible to be distinguished.
One should always question the performance of the approaches they apply and try alternative methods.
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