Question

How can biased sampling affect the statistical study of a population?

Answer 1

Biased sampling can lead to inaccurate and unreliable conclusions in a statistical study because it fails to represent the wider population fairly. Large sample sizes do not overcome the effects of a biased sample, and careful random selection is necessary to ensure accuracy. Response bias also affects results, highlighting the need for truthful survey participation.

Step-by-step explanation:

Biased sampling can significantly affect the statistical study of a population. If the sample collected does not accurately represent the wider population because certain members are less likely to be chosen, the study's conclusions may be invalid. This lack of representation can be due to a range of factors, including convenience sampling, selection bias, or a sampling error. For example, conducting a survey of students only during a specific lunchtime hour excludes those not present at that time, thus creating a bias.

Biased samples undermine the reliability and validity of statistical results. Reliability refers to the consistency of a measurement, while validity concerns the accuracy of the conclusions drawn from the study. A large sample size does not compensate for biased sampling techniques; both chance error and bias need to be addressed by selecting samples that are random and representative of the entire population. Internet surveys are particularly prone to bias since participants are self-selecting.

To mitigate biased sampling, it is essential that samples are randomly selected and representative of the population being studied. This ensures that any conclusions drawn from the data are likely to be more accurate and reliable. Responding to surveys truthfully is also critical to avoid response bias, where inaccuracies arise from participants reporting what they think is favorable rather than their true opinions.

Answer 2

Down below

Step-by-step explanation:

Biased sampling can affect the statistical study of a population by not having a variety of samples and there would either be all evidence supporting one side, but not talking about the other. Example: Someone is put on jury duty and knows the person being accused. That would be an example of biased because whether you know the person in a good or bad way, you decesion on if they are guilty or not guilty would naturally be on how you feel about them. Although this example doesn't pertain to the question given, the biased sampling would be innaccurate. A better example would be if a scientist wanted to know if all people with the flu were depressed. Let's say that scientist only asked depressed people with the flu if they were depressed, that would be innaccurate and the conclusion might be that all people with the flu are depressed.

Sorry if my explanation confused you.