Answer:In 1900, communicable diseases, like pneumonia, TB, influenza, chicken pox, and polio, were the most common causes of death. In 2006, lifestyle diseases, like cancer, heart disease, and stroke, were the most common causes of death
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This is an observational study as the health worker did not conduct any experiment but instead, she observed and studied patients data and drew her inferences from the available data.
A confounding is an external variable in an experiment that can change the effect of the dependent and independent variable. They can ruin an experiment if not accounted for and suggest false correlations, and introduce bias as it is one of the sources of bias in epidemiological studies. For example, using the study above, it is said that the cholesterol reading of the group not taking omega-3 fish oil is 18 points higher than the other; a confounding variable would be like an extra independent variable unaccounted for which could influence the outcome of the study like the age of the patients, family history, diet etc. All these extra variables can influence the cholesterol level of the patients but yet were unaccounted for and if inferences are made from the observations, the correlations could be false.
Even though the 18 point difference is statistically significant, they cannot conclude that omega-3 fish oil is the cause because of the effect of confounding in experiments and the many external variables like lifestyle, diet, smoking etc. which could influence the outcome of the research that were unaccounted for
