What does cherry-picking mean in the context of data analytics?Select an answer:lack of randomnessconfirmation biassampling errorselection bias
Question
What does cherry-picking mean in the context of data analytics?
Select an answer:
- lack of randomness
- confirmation bias
- sampling error
- selection bias
Solution
Answer: Confirmation Bias
In the context of data analytics, "cherry-picking" refers to the practice of selectively presenting data or results that support a particular argument or conclusion while ignoring those that might contradict it. This behavior is often linked to confirmation bias, where individuals favor information that aligns with their pre-existing beliefs or hypotheses. By focusing only on the "cherries" or favorable data points, analysts can create a misleading representation of the dataset, ultimately leading to skewed insights and flawed decision-making. Therefore, among the options provided, "confirmation bias" is the most accurate definition of cherry-picking in data analytics.
Similar Questions
The fewer the biases in measurement and in data collection procedures, the morescientific the research. Comment on this statement
Analyze potential biases in employee selection processes which could be influenced by inherent biases in the system and also due to limited sample sizes
_______ refers to a situation in which some unit selected for a sample refuses to participate. CensusNon-response Unit errorBias
Which type of bias refers to looking for evidence to prove a hypothesis you have?1 pointSunk cost fallacyConfirmation biasPrimacy biasFalse consensus bias
What is it called when AI algorithms show prejudice due to biased data they were trained on?AI FallacyAI ErrorAI BiasAI MisjudgmentI don't knowSubmit
Upgrade your grade with Knowee
Get personalized homework help. Review tough concepts in more detail, or go deeper into your topic by exploring other relevant questions.