What is the most common cause of violation of the model assumption of independence?

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BlackTom AI · Accepted Answer

Question restatement: What is the most common cause of violation of the model assumption of independence?

Option 1: poor study design. This option points to problems in how the data were collected or organized, such as clustering, repeated measurements on the same subjects, or other dependencies introduced by the design. When observations are not independent of each other due to the way the study was conducted, the independence assumption is violated more readily than in a well-planned design. In many practical analyses, lack of independence arises from design choices like enrolling related individuals, time-ordered measurements without proper models, or hierarchical sampling. This makes it a strong and common culprit.

Option 2: sampling error. Sampling error relates to the difference between the sample and the population due to random variation. While sampling error affects estimates and generalizability, it does not inherently create dependence among observations within the sample. Independence concerns are more about how observations relate to one another within the data, not about how representative the sample is relative to the population.

Option 3: homoscedasticity. Homoscedasticity refers to constant variance of the residuals across levels of an independent variable. It is a violation concern tied to variance structure, not to whether observations are independent. A model can have homoscedastic residuals while still violating independence, or vice versa.

Option 4: known and accounted for confounding factors. If confounding factors are known but properly controlled for (e.g., through design, stratification, or covariate adjustment), independence is less likely to be violated due to those confounders. In contrast, failing to account for confounders can sometimes induce dependence structures, but the phrase “known and accounted for” implies that those factors are addressed, reducing the risk of independence violations arising from confounding.

Summary: Among the options, the design-related issue described in option 1 most directly explains why independence assumptions are violated in many analyses, making it the most plausible and common cause. The other options describe different statistical concerns that do not inherently cause or explain dependence among observations in the same way.

AGRI90075_2025_SM1 Week 6 Quiz - Model Assumptions

What is the most common cause of violation of the model assumption of independence?

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