Bias in data typically occurs when:

Bias in data typically occurs when the experimenter seeks to prove a hypothesis as true because this can lead to selective reporting and interpretation of results. When researchers have a preconceived notion or objective, they might unintentionally favor data that supports their hypothesis while overlooking or disregarding data that contradicts it. This desire to validate a specific outcome can distort the research process and findings, thereby leading to a skewed representation of the actual situation.

In contrast, poorly designed experiments can lead to flawed results, but the presence of bias specifically relates to the motivations and actions of the researcher. A large sample size tends to enhance the reliability of data rather than introduce bias, as it allows for better generalization of results. Lastly, accurate recording of results is crucial for maintaining data integrity, but the presence of bias itself arises from the researcher’s intentions rather than the accuracy of their data collection.