Biased Sample:

A biased sample refers to a subset of individuals or elements in a population that does not accurately represent the entire population due to systematic errors or prejudices in the selection process. Biased samples can introduce misleading results and compromise the validity and generalizability of statistical findings.

Causes of Bias:

Biases in sample selection can arise from various factors, including:

• Non-random sampling methods
• Sampling from a restricted or non-representative population
• Voluntary response or self-selection bias
• Undercoverage or overcoverage of certain groups
• Sampling based on convenience or accessibility

Types of Bias:

There are different types of bias that can affect the sample:

• Selection Bias: Occurs when some members of the population have a higher chance of being included in the sample than others, leading to an unrepresentative subset.
• Sampling Frame Bias: Arises when the list or database used to select the sample is incomplete or inaccurate, failing to include all relevant members of the population.
• Non-Response Bias: Occurs when individuals who choose not to participate in the survey or study significantly differ from those who do, resulting in an unrepresentative sample.
• Response Bias: Refers to a bias introduced due to the way participants respond to questions or stimuli, influencing the accuracy and reliability of the collected data.

Impact and Mitigation:

Using a biased sample can lead to exaggerated or misleading results, limiting the extent to which findings can be generalized to the entire population. To mitigate bias, researchers strive to employ random sampling methods, ensuring that every member of the population has an equal chance of being selected. Additionally, careful consideration of the specific biases that may affect a particular study can help improve the accuracy and reliability of the sample and subsequent findings.