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Data manipulation is a serious issue/consideration in the most honest of statistical analyses. Outliers, missing data and non-normality can all adversely affect the validity of statistical analysis. It is appropriate to study the data and repair real problems before analysis begins.
The sea level data in the post does not show annual mean – or average – sea levels at Fort Denison. The actual annual average sea level in that area was higher in 2019 than in 1914, according ...
In statistics, a misleading graph, also known as a distorted graph, is a graph that misrepresents data, constituting a misuse of statistics and with the result that an incorrect conclusion may be derived from it. Graphs may be misleading by being excessively complex or poorly constructed.
A 2009 systematic review and meta-analysis of survey data found that about 2% of scientists admitted to falsifying, fabricating, or modifying data at least once. [ 3 ] Incidents should only be included in this list if the individuals or entities involved have their own Wikipedia articles, or in the absence of an article, where the misconduct ...
Most investors heaved a sigh of relief when the nation's gross domestic product, a broad measure of economic activity, rose 3.5% in the third quarter, signaling that the recession had ended. But ...
Some scholars classify cherry-picking as a fallacy of selective attention, the most common example of which is the confirmation bias. [3] Cherry picking can refer to the selection of data or data sets so a study or survey will give desired, predictable results which may be misleading or even completely contrary to reality. [4]
False precision (also called overprecision, fake precision, misplaced precision and spurious precision) occurs when numerical data are presented in a manner that implies better precision than is justified; since precision is a limit to accuracy (in the ISO definition of accuracy), this often leads to overconfidence in the accuracy, named precision bias.
Visualization of Simpson's paradox on data resembling real-world variability indicates that risk of misjudgment of true causal relationship can be hard to spot. Simpson's paradox is a phenomenon in probability and statistics in which a trend appears in several groups of data but disappears or reverses when the groups are combined.