I need these Questions in about 150 words each with referen…

I need these Questions in about 150 words each with references 1. Does the sample size impact bias or confounding? Will a large study have less bias than a small study? Please justify your rationale. 2. How can the reader of a study discern if bias has been accounted for and minimized?

 

Answered

The Impact of Sample Size on Bias and Confounding: A Comparative Analysis

1. The Influence of Sample Size on Bias and Confounding

One crucial consideration in research design is the sample size, as it has implications on the potential for bias and confounding. Bias refers to systematic errors that cause results to deviate from the truth, while confounding occurs when an extraneous variable distorts the relationship between the exposure and outcome of interest. The impact of sample size on bias and confounding is dependent on several factors.

A large study, by virtue of a larger sample size, may seem intuitive to produce less bias than a small study. However, it is essential to recognize that the impact of sample size on bias and confounding is complex. Although a larger sample size can mitigate sampling error and enhance the precision of estimates, it does not inherently guarantee the reduction of bias. Bias can arise from various sources such as selection bias or measurement error, which are not necessarily alleviated by increasing sample size alone.

To justify this rationale, consider a study investigating the association between a specific drug and a health outcome, where patients are self-selected volunteers. Increasing the sample size may not address potential biases related to the characteristics and behaviors of those who chose to participate. Hence, a large study size does not automatically obviate biases arising from non-random sampling or other design flaws.

Academic sources:
1. Rothman, Kenneth J., Sander Greenland, and Timothy L. Lash. Modern epidemiology. Lippincott Williams & Wilkins, 2008.
2. Grimes, David A., and Kenneth F. Schulz. “Bias and causal associations in observational research.” The Lancet 359.9302 (2002): 248-252.

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