# Lurking variable. Lurking Variable in Statistics: Definition & Example 2022-10-27

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A lurking variable, also known as a confounding variable, is a variable that is not directly observed or controlled in a study, but may affect the relationship between the variables being studied. This can lead to misleading results and incorrect conclusions about the relationship between the variables.

For example, consider a study that is examining the relationship between exercise and weight loss. The researchers may control for factors such as age, gender, and diet, but there may be other factors that they are not aware of or that they do not measure, such as genetics or stress levels. These factors may affect the relationship between exercise and weight loss, leading to inaccurate conclusions about the effectiveness of exercise for weight loss.

To control for lurking variables, researchers can use a variety of methods, such as random assignment, stratified sampling, and controlling for multiple variables in statistical analyses. However, it is not always possible to completely eliminate the influence of lurking variables, and it is important for researchers to be aware of the potential for confounding and to consider it when interpreting their results.

Lurking variables can also be a problem in everyday life, as we may be influenced by factors that we are not aware of or that we do not consider when making decisions or drawing conclusions. It is important to be aware of the potential for lurking variables and to try to control for them as much as possible in order to make more accurate judgments and decisions.

In summary, lurking variables are important to consider in research and everyday life because they can affect the relationship between variables and lead to incorrect conclusions. It is important to be aware of the potential for lurking variables and to use methods to control for them in order to make more accurate judgments and decisions.

## Lurking Variables: Some Examples

In this report several examples of lurking variables are given. Does pollution cause global warming? The international students' median score about 700 exceeds the third quartile of U. Lurking variables are sometime referred to as "common response". When we supplement the two-way table with the conditional percents within each hospital:we find that Hospital A has a higher death rate 3% than Hospital B 2%. This common effect creates the observed association between the explanatory and response variables, even though there is no causal link between them. In contrast, only 200 out of 800 patients at Hospital B were severely ill.

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## Lurking Variables: Definition & Examples

The lurking variable here is the increase in disposable income. In other words, you might be tempted to interpret the observed association as causation. That has everything to do with age and infirmity, and nothing to do with the marriages. The following figure will help you visualize this situation: In particular, as in our example, the lurking variable might have an effect on both the explanatory and the response variables. But researchers can use the observed association as a first step in building a case for causation. In other words, the explanatory variable and the response variable vary together in a predictable way. Here's an example of a lurking variable versus a confounding variable.

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## Lurking Variable in Statistics: Definition & Example

More serious fires require more firefighters and also result in more damage. It is a confounding or not variable. It's very important to identify what lurking variables in statistics. Identify lurking variables that may explain an observed relationship. In experimental studies, however, the impact of lurking variables can mostly be eliminated with good experimental design. Lurking variables are a huge threat to internal validity, because they're unknown at the start of a study and weren't controlled for in its design. Does this mean that volunteers are causing more damage to occur? This common effect creates the observed association between the explanatory and response variables even though there is no cause-and-effect link between them.

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## Blocking in Statistics: Definition & Example

Someone who's been married three times is statistically likely to die before someone who's only been married twice. Does this mean that gloves are causing more snowboard accidents to occur? This means any differences in blood pressure can be attributed to the pill, rather than the effect of a lurking variable. A lurking variable is a variable that is not measured in the study. We will use a simplified version of this study to illustrate the researchers' claim, and see what the possible effect could be of including a lurking variable in a study. In the case of a linear relationship, people mistakenly interpret an r-value that is close to 1 or -1 as evidence that the explanatory variable causes changes in the response variable. Essentially, lurking variables can cause the results of a study to be misleading.

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## Lurking Variables: Some Examples on JSTOR

These are the questions we are going to discuss next. All of these questions imply a cause-and-effect relationship in situations that are complex and involve many interacting variables. Values near 0 can indicate a weak or no linear relationship. To establish a cause-and-effect relationship, researchers must conduct a comparative randomized experiment. However, once we split the individuals into two blocks based on gender, it becomes apparent that the new diet does seem to be associated with more weight loss: By placing the individuals into blocks, the relationship between the new diet and weight loss became more clear since we were able to control for the nuisance variable of gender. A lurking variable is a variable that is hidden or not included in an analysis, but impacts the relationship being analyzed.

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## Causation and Lurking Variables (2 of 2)

Alone, each study can show only an association. To investigate the connection between cigarette consumption and lung cancers, the data is offset by 30 years because cancer takes time to develop. In an experiment, though, the factor under consideration isn't being driven by some lurking variable, because we are the ones in control there. A lurking variable is a variable that is not included in a statistical analysis, yet impacts the relationship between two variables within the analysis. Note that in each of the above two examples, the lurking variable interacts differently with the variables studied. Values near 1 indicate a strong positive linear relationship. In the scatterplot, we see a fairly strong positive correlation.

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## Lurking Variable Concept & Examples

This is a threat to both the validity of the research and the researcher's conclusions. How to Identify Lurking Variables To discover lurking variables, it helps to have domain expertise in the area under study. If you have no information about what the data actually looks like, then you should not use the correlation coefficient in your analysis. When asking the students how they prepared for the paper, students replied with different answers. As temperature decreases, more people buy gloves and more people go snowboarding. However, this researcher forgot about the confounding variable.

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## 2.7 Causation and Lurking Variables Flashcards

Lurking variables are the risk we face in sampling and observational studies. There is an association between the variables. The distinction between these two types of interactions is not as important as the fact that in either case, the observed association can be at least partially explained by the lurking variable. Another researcher reads this and decides to look for something else that might connect this data. Whenever including a lurking variable causes us to rethink the direction of an association, this is an instance of Simpson's paradox.

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