Confirmation bias: Perception affects actual outcomes throughout data processing. This viewpoint leads to confirmation bias, that can affect the results. Confirmation bias doesn’t happen since there isn’t enough data, according to research. Evidence scientists and analysts frequently choose data that confirms their own beliefs, worldviews, and points of view.
Typically, when filtering information, they will concentrate on acquiring facts that are consistent with their idea or hypothesis and avoid any facts that even remotely contradict it. Data scientists must eliminate data that doesn’t fit their preset worldview.
It’s essential to approach recent information with objectivity. Organizations having a character for being authoritarian and giving priority to their own perceptions are exhibiting this behaviour more frequently. You should pay additional attention to disconfirming facts since confirmation bias frequently has negative effects on business outcomes.
Selection bias: This happens when the features of the sample data that are gathered and processed for modelling do not adequately represent the real, future population of cases that the model would encounter. In other words, whenever a subset of a data is purposely (i.e., not randomly) excluded from the study, selection bias arises.
As a result, the initial sample, which was carefully selected, no longer correctly represents the larger population. For instance, United States Government routinely conducts a census to provide government organisations with essential demographic information about the population at a certain time. However, the economic theories based on that data likewise become out of date.
If the old population is still used, the data become biassed. But there are a few strategies that can be applied to decrease selection bias. When a data sample is taken, the sampling design should be noted, and any technique constraints should be made explicit. The potential of selection bias will be highlighted in this documentation once the model has been created and put into use.
