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Questions and Answers
What is one common technique used to make differences in data appear more significant than they are?
Which type of manipulation involves selectively presenting data that supports a desired outcome while ignoring contradictory data?
What problem occurs when an external variable influences the correlation between two studied variables?
What bias occurs when survey questions are phrased to elicit a specific response?
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Which term describes the elimination of data points that do not fit the desired outcome without a valid reason?
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What issue arises when respondents provide answers they believe are more socially acceptable rather than their true opinions?
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What occurs when the measure of one variable seems to change another, but it's the other way around?
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What bias is introduced when a sample does not adequately represent the population?
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Study Notes
Deceptive Statistics
Misleading Graphs
- Truncated scales: Graphs with truncated scales can make differences appear more significant than they are.
- Misleading axes: Axes with unconventional or non-linear scales can distort the representation of data.
- Cherry-picked data: Selectively presenting only a portion of the data to support a claim, while ignoring the rest.
- 3D effects: Using 3D effects to make graphs more visually appealing, but potentially misleading.
Data Manipulation
- Data dredging: Searching for patterns in large datasets without a prior hypothesis, leading to false discoveries.
- Data mining: Selectively presenting only the data that supports a claim, while ignoring the rest.
- Outlier removal: Eliminating data points that do not fit the desired pattern, without a valid reason.
- Data smoothing: Over-smoothing data to hide variability, making trends appear more significant than they are.
Correlation Vs Causation
- Correlation does not imply causation: A statistical relationship between two variables does not necessarily mean one causes the other.
- Third variable problem: A third variable may be causing the observed correlation between two variables.
- Reverse causality: The supposed effect may actually be the cause, and vice versa.
- Common method bias: Measurement tools or methods may be influencing the observed correlation.
Survey Biases
- Sampling bias: The sample may not be representative of the population, leading to biased results.
- Non-response bias: Non-response rates may be higher among certain groups, leading to biased results.
- Social desirability bias: Respondents may provide answers they think are socially acceptable, rather than their true opinions.
- Leading questions: Questions may be phrased to elicit a specific response, rather than an honest opinion.
- Response bias: Respondents may answer questions based on their current mood or situation, rather than their true opinions.
Deceptive Statistics
Misleading Graphs
- Graphs with truncated scales exaggerate differences by cutting off the bottom or top of the scale, making the data appear more extreme.
- Misleading axes use unconventional or non-linear scales, distorting the representation of data and creating a false narrative.
- Cherry-picked data is selectively presented to support a claim, while ignoring the rest of the data, which can be misleading and biased.
- 3D effects make graphs more visually appealing, but can also make the data more difficult to interpret and potentially misleading.
Data Manipulation
- Data dredging involves searching for patterns in large datasets without a prior hypothesis, leading to false discoveries and biased conclusions.
- Data mining selectively presents only the data that supports a claim, while ignoring the rest, which is a form of confirmation bias.
- Outlier removal eliminates data points that do not fit the desired pattern, without a valid reason, which can distort the true representation of the data.
- Data smoothing over-smoothes data to hide variability, making trends appear more significant than they are, which can lead to false conclusions.
Correlation Vs Causation
- Correlation does not imply causation, meaning that a statistical relationship between two variables does not necessarily mean one causes the other.
- The third variable problem occurs when a third variable causes the observed correlation between two variables, which can be misleading.
- Reverse causality occurs when the supposed effect actually causes the supposed cause, which can lead to false conclusions.
- Common method bias occurs when measurement tools or methods influence the observed correlation, leading to biased results.
Survey Biases
- Sampling bias occurs when the sample is not representative of the population, leading to biased results and false conclusions.
- Non-response bias occurs when non-response rates are higher among certain groups, leading to biased results and inequitable representation.
- Social desirability bias occurs when respondents provide answers they think are socially acceptable, rather than their true opinions, which can lead to biased results.
- Leading questions are phrased to elicit a specific response, rather than an honest opinion, which can lead to biased results and false conclusions.
- Response bias occurs when respondents answer questions based on their current mood or situation, rather than their true opinions, which can lead to biased results and false conclusions.
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Description
This quiz tests your ability to identify misleading and deceptive statistics in graphs, including truncated scales, misleading axes, cherry-picked data, and 3D effects. Learn to critically analyze graphical data and avoid common pitfalls.
