Final answer:
Comorbidity exists when individuals have multiple medical conditions at the same time. It can arise due to shared risk factors, common biological mechanisms, or the presence of one condition increasing the likelihood of another. Sampling bias can affect the estimation of comorbidity rates if the sample used in a study is not representative of the population.
Step-by-step explanation:
In statistics, comorbidity exists when individuals have multiple medical conditions or disorders at the same time. It is often observed that certain conditions tend to co-occur more frequently than would be expected by chance. Comorbidity can arise due to a variety of factors, including shared risk factors, common biological mechanisms, or the presence of one condition increasing the likelihood of developing another.
Sampling bias refers to the error introduced when the sample used in a study is not representative of the entire population. It can potentially affect the estimation of comorbidity rates if the sample is not chosen randomly or if it does not accurately reflect the diversity of the population. For example, if a study on comorbidity only includes participants from a specific age group or geographical location, it may not capture the true prevalence of comorbid conditions in the broader population.