A.3 explain how statistics can be used to misleading
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Remember, misuse of statistics can be accidental or purposeful. While a malicious intent to mislead and misuse data with misleading statistics will surely magnify bias, intent is not necessary to create statistical misunderstands. The misuse of statics is a much broader problem that now permeates through multiple industries and fields of study. Here are a few potential statistical mishaps that commonly lead to misuse:
1. Faulty Polling:
The manner in which questions are phrased can have a huge impact on the way an audience answers them. Specific wording patterns have a persuasive effect and induce respondents to answer in a predictable manner. For example, on a poll seeking tax opinions.
2. Flawed Correlations:
The problem with correlations is this: If you measure enough variables, eventually it will appear that some of them correlate. As one out of twenty will inevitably be deemed significant without any direct correlation, studies can be manipulated (with enough data) to prove a correlation that does not exist or that is not significant enough to prove causation.
3. Data Fishing:
This misleading data example is also referred to as “data dredging” (and related to flawed correlations). It is a data mining technique where extremely large volumes of data are analyzed for the purposes of discovering relationships between data points. Seeking a relationship between data isn’t a data misuse per se, however, doing so without a hypothesis is.
4. Misleading Data Visualization:
Insightful statistical graphs and charts include very basic, but essential, grouping of elements.
5. Purposeful Bias:
The last of our most common examples for misuse of statistics and misleading data is, perhaps, the most serious. Bias is the deliberate attempt to influence data findings without even feigning professional accountability. Bias is most likely to take the form of data omissions or adjustments.
1. Faulty Polling:
The manner in which questions are phrased can have a huge impact on the way an audience answers them. Specific wording patterns have a persuasive effect and induce respondents to answer in a predictable manner. For example, on a poll seeking tax opinions.
2. Flawed Correlations:
The problem with correlations is this: If you measure enough variables, eventually it will appear that some of them correlate. As one out of twenty will inevitably be deemed significant without any direct correlation, studies can be manipulated (with enough data) to prove a correlation that does not exist or that is not significant enough to prove causation.
3. Data Fishing:
This misleading data example is also referred to as “data dredging” (and related to flawed correlations). It is a data mining technique where extremely large volumes of data are analyzed for the purposes of discovering relationships between data points. Seeking a relationship between data isn’t a data misuse per se, however, doing so without a hypothesis is.
4. Misleading Data Visualization:
Insightful statistical graphs and charts include very basic, but essential, grouping of elements.
5. Purposeful Bias:
The last of our most common examples for misuse of statistics and misleading data is, perhaps, the most serious. Bias is the deliberate attempt to influence data findings without even feigning professional accountability. Bias is most likely to take the form of data omissions or adjustments.
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