Final answer:
Multiple observations are necessary to increase accuracy, reduce bias, and develop robust models, ensuring that scientific conclusions are reliable and representative of true population characteristics.
Step-by-step explanation:
Multiple observations are needed in scientific research for several key reasons. First, having a large sample size increases the chance of obtaining a good estimate of the population's central tendency, such as the mean or median, and its variance, which is the spread of true values. It also helps to accurately capture the distribution, such as a Normal or binomial distribution, within a population.
Secondly, in observational research, there is a potential for observer bias, where those conducting the observations may unconsciously record data to fit their expectations. Establishing clear criteria for the behaviors recorded and comparing observations by multiple observers can help mitigate this bias and increase the reliability of the results, known as inter-rater reliability. Lastly, diversity in observations helps in developing robust models, such as the Ricker-Allee model in conservation biology, that can accommodate multiple working hypotheses and provide better abstractions for complex systems. This is particularly important when experimental manipulation is not feasible, as is the case in many ecological and biological studies.