![]() ![]() Suppose a researcher is studying the amount of salts in a lake by averaging at different locations. For example, a predefined range can be defined from most parameters that can be obtained in theory. Therefore simple checks should be run that are quite effective in eliminating the abnormalities. Raw data processing can be a time consuming task and it is not always easy to catch anomalies. Therefore raw data processing would be required in order to correctly extract the information required without errors. For example, census data provides a wealth of geographic and demographic data, but a researcher might need only certain segments of the data from certain locations. It is also important to extract exactly the information that is needed from the overall experiment. Participants may also have checked the wrong answer or may have simply misunderstood or skipped a question. ![]() For example, the researcher finds an error in a question which makes it invalid. In social experiments involving surveys, there are a number of possibilities why a given data set might need to be edited or processed. Statistical raw data processing needs to be carried out in this case to eliminate this data point in order to ensure accuracy of the conclusions drawn based on the experiment. This may be due to a sudden surge in voltage in the source, and this data point is therefore a deviant. Removal of Outliers While measuring the current flow through a resistor with the help of an ammeter, there may be one data point that is far away from the rest, an statistical outlier. ![]()
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