What do you mean by errors? What is the effect of errors on analysis of a sample? How can these be detected and minimised?

Short Answer

Expert verified
Errors refer to inaccuracies in data. They can majorly skew the data analysis leading to incorrect conclusions. By employing techniques like cross-verification, outlier detection, applying statistical methods, and regular auditing, these errors can be detected and minimized thereby ensuring accuracy and reliability of the data analysis.

Step by step solution

01

Definition of Errors

Errors, in the context of data analysis, refer to the inaccuracies or discrepancies that occur in data or results due to various factors. These could be human errors, instrument errors, or environmental variables that harm the precision or accuracy of the data.
02

Effects of Errors

Errors can severely impact the analysis of a sample. They can lead to inaccuracies in the results, misleading conclusions, and wrong inferences. Especially in research, errors can lead to false hypotheses or assumptions, undermining the integrity and reliability of the study.
03

Detection and Minimization of Errors

Errors can be detected through various methods such as cross-verification, determining the outlier data, applying statistical methods, and reviewing the process. Techniques to minimize errors include proper calibration of instruments, regular training of personnel involved in data collection, applying rigorous scientific methodology in analysis, using statistical tools for error detection, and regular auditing.

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