Podcast
Questions and Answers
What is one common technique used to make differences in data appear more significant than they are?
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?
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 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?
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?
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?
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?
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?
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.
