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Multiple comparisons arise when a statistical analysis involves multiple simultaneous statistical tests, each of which has a potential to produce a "discovery". A stated confidence level generally applies only to each test considered individually, but often it is desirable to have a confidence level for the whole family of simultaneous tests. [ 4 ]
However, the horse was unable to answer correctly when either it could not see the questioner, or if the questioner themselves was unaware of the correct answer: When von Osten knew the answers to the questions, Hans answered correctly 89% of the time. However, when von Osten did not know the answers, Hans guessed only 6% of questions correctly.
Triangulation will considerably increase the confidence in a study tremendously. There are a few ways triangulation can occur, including the use of multiple observers, which is a form of reliability in itself called interobserver reliability, measured by the percentage of times that the observers agree.
An experimenter's confirmation bias can potentially affect which data are reported. Data that conflict with the experimenter's expectations may be more readily discarded as unreliable, producing the so-called file drawer effect. To combat this tendency, scientific training teaches ways to prevent bias. [97]
Extreme responding is a form of response bias that drives respondents to only select the most extreme options or answers available. [1] [17] For example, in a survey utilizing a Likert scale with potential responses ranging from one to five, the respondent may only give answers as ones or fives. Another example is if the participant only ...
Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. [8] It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.
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Multiple testing procedures are sometimes used to compensate, but that is often difficult or impossible to do precisely. Post hoc analysis that is conducted and interpreted without adequate consideration of this problem is sometimes called data dredging by critics because the statistical associations that it finds are often spurious.