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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. "[I]n any scatter diagram there will be some points more or less detached from the main part of the cloud: these points should be rejected only for cause."
The specific reasons why misinformation spreads through social media so easily remain unknown. A 2018 study of Twitter determined that, compared to accurate information, false information spread significantly faster, further, deeper, and more broadly. [34]
Undetected data corruption, also known as silent data corruption, results in the most dangerous errors as there is no indication that the data is incorrect. Detected data corruption may be permanent with the loss of data, or may be temporary when some part of the system is able to detect and correct the error; there is no data corruption in the ...
Statistical bias exists in numerous stages of the data collection and analysis process, including: the source of the data, the methods used to collect the data, the estimator chosen, and the methods used to analyze the data. Data analysts can take various measures at each stage of the process to reduce the impact of statistical bias in their ...
For this essay to apply, we need to be able to show, unambiguously, that a source is either wrong or very likely to be wrong. If you are reading this because you think the cold streak you've been having disproves climate change, this essay is not for you. This essay concerns cases where, based on an analysis of existing reliable sources for a ...
A famous example of verifiable material that is potentially inaccurate is the front page of the Chicago Tribune on November 3, 1948—we have an article about this headline at "Dewey defeats Truman". In this case, we have a retraction from the newspaper which provides strong evidence that the material was inaccurate.
Information may be structured as data. Redundant data can be compressed up to an optimal size, which is the theoretical limit of compression. The information available through a collection of data may be derived by analysis. For example, a restaurant collects data from every customer order.
In the design-based approach, the model is taken to be known, and one of the goals is to ensure that the sample data are selected randomly enough for inference. Statistical assumptions can be put into two classes, depending upon which approach to inference is used. Model-based assumptions. These include the following three types: