However, investigators quickly realized that a woman with a large family tends to be relatively young at the birth of her first child. Subsequent studies showed that it was young age at first birth, not number of full-term pregnancies, that reduces breast cancer risk. The apparent association between large families and lower breast cancer risk was the result of the confounding effect of age at first birth. There are a number of factors that can provide misleading evidence. Confounding factors, such as those in the example above, might lead researchers to find an association for the wrong reason.
Selection bias is another concern.
If a health investigator interviews individuals in a wealthy suburb about their diet, they might get very different answers than from a population in a rural area. Epidemiological studies are based on a sample population selected from the larger population. This inevitably leads to some uncertainty in the results. Any sample will be different, at least a little, from the whole population, and the smaller the population, the more difficult it is to draw conclusions about the larger population. Epidemiologists, therefore, perform mathematical tests to assess whether the results are statistically significant, or whether the results could have occurred by chance.