Critical Evaluation- Intro Statistics
Openstax
Read Ahead Community
Critical Evaluation
We need to evaluate the statistical studies we read about critically and analyze them before accepting the results of the studies.
Common problems to be aware of include:
Problems with samples: A sample must be representative of the population. A sample that is not representative of the population is biased. Biased samples that are not representative of the population give results that are inaccurate and not valid.
Self-selected samples: Responses only by people who choose to respond, such as call-in surveys, are often unreliable.
Sample size issues: Samples that are too small may be unreliable. Larger samples are better, if possible. In some situations, having small samples is unavoidable and can still be used to draw conclusions.
Examples: crash testing cars or medical testing for rare conditions
Undue influence: collecting data or asking questions in a way that influences the response
Non-response or refusal of subject to participate: The collected responses may no longer be representative of the population.
Often, people with strong positive or negative opinions may answer surveys, which can affect the results.
Causality: A relationship between two variables does not mean that one causes the other to occur.
They may be related (correlated) because of their relationship through a different variable.
Self-funded or self-interest studies: A study performed by a person or organization in order to support their claim. Is the study impartial? Read the study carefully to evaluate the work. Do not automatically assume that the study is good, but do not automatically assume the study is bad either. Evaluate it on its merits and the work done.
Misleading use of data: improperly displayed graphs, incomplete data, or lack of context
Confounding: When the effects of multiple factors on a response cannot be separated.
Confounding makes it difficult or impossible to draw valid conclusions about the effect of each factor
Download for free at
http://cnx.org/content/col11562/latest/
We need to evaluate the statistical studies we read about critically and analyze them before accepting the results of the studies.
Common problems to be aware of include:
Problems with samples: A sample must be representative of the population. A sample that is not representative of the population is biased. Biased samples that are not representative of the population give results that are inaccurate and not valid.
Self-selected samples: Responses only by people who choose to respond, such as call-in surveys, are often unreliable.
Sample size issues: Samples that are too small may be unreliable. Larger samples are better, if possible. In some situations, having small samples is unavoidable and can still be used to draw conclusions.
Examples: crash testing cars or medical testing for rare conditions
Undue influence: collecting data or asking questions in a way that influences the response
Non-response or refusal of subject to participate: The collected responses may no longer be representative of the population.
Often, people with strong positive or negative opinions may answer surveys, which can affect the results.
Causality: A relationship between two variables does not mean that one causes the other to occur.
They may be related (correlated) because of their relationship through a different variable.
Self-funded or self-interest studies: A study performed by a person or organization in order to support their claim. Is the study impartial? Read the study carefully to evaluate the work. Do not automatically assume that the study is good, but do not automatically assume the study is bad either. Evaluate it on its merits and the work done.
Misleading use of data: improperly displayed graphs, incomplete data, or lack of context
Confounding: When the effects of multiple factors on a response cannot be separated.
Confounding makes it difficult or impossible to draw valid conclusions about the effect of each factor
Download for free at
http://cnx.org/content/col11562/latest/