Understanding Social and Survivorship Biases

• Understanding Bias

Social Desirability Bias

• This bias occurs when survey respondents answer according to societal expectations instead of their own beliefs.
• Social Desirability Bias impacts Gathering data and communicating resultant data.
• Example: In a project about top songs, someone might list more popular songs to avoid judgment.

Survivorship Bias

• This bias involves focusing on successful outcomes or "survivors" while ignoring those who did not succeed.
• Survivorship bias impacts Gathering data and communicating resultant data.
• Example: Focusing on wealthy tech entrepreneurs who dropped out of college may create the impression that a college education is unnecessary for a tech career.

Selection Bias

• This bias happens when the sample studied doesn't accurately represent the population being analyzed.
• Selection bias is an umbrella term that can encompass survivorship, volunteer bias, etc.
• Selection bias impacts data gathering.
• Example: Data collected from high-income students about water usage would not accurately reflect the population's water usage with income diversity.

Volunteer Bias

• This bias occurs when survey participants self-select into a sample, and those who volunteer differ from those who don't.
• Volunteer bias impacts asking questions, gathering data, and analyzing the gathered data.
• Example: Responses to a survey sent to a community may be skewed because of the fact that most responses only came from parents because staff didn't want to fill it out.

Funding Bias

• This bias happens when researchers distort results or alter conclusions due to pressure from commercial or non-profit funders.
• Funding bias impacts analyzing/synthesizing, and communicating results.
• Example: manipulating results in a skin tone magazine project to claim colorism isn't an issue, influenced by a magazine that practices colorism giving funding.

Recall Bias

• This bias surfaces when participants struggle to accurately remember past events or omit details when reporting in clinical trials or research studies.
• Recall bias impacts gathering and organizing data, and communicating results.
• Example: A sick patient may overestimate the times they feel healthy and underestimate how often they feel sick, leading to a discrepancy between their perception and reality.

Omitted Variable Bias

• Omitting a key variable from a model is what causes this bias.
• Omitted variable bias impacts the model, analyze, and synthesis parts of the data project.
• Example: During a water usage project, excluding the data from how many people lived in the house would be an example of omitted bias

Nonresponse Bias

• This occurs when individuals who are unable or unwilling to participate in a research study possess different data compared to those who do participate.
• Nonresponse bias impacts Gathering Data, Analyzing/Synthesizing Data, and End Results.
• Example: Health surveys where people opt out of certain questions or report inflated better health outcomes.

Reporting Bias

• This bias involves suppressing or selectively revealing specific information.
• Reporting bias will impact Gathering and Organizing Data, Analyzing and Synthesizing data, and Communicating Results.
• You might arrive at faulty conclusions because you are reporting on incorrect data
• Example: Collecting the incorrect data on a skin tone project, additionally, when you were were collecting the data you may have skipped certain pages if there were not many people present.

Other Bias

• This encompasses favoritism or prejudice toward a particular gender.
• This may include impacts to Asking Questions, Gathering & Organizing data, Analysis and Synthesis and communicating results.
• Example: If most data is collected on men in a study, the results may not be very helpful/ applicable to women.

Explore social desirability, and survivorship biases that impact data collection. Social Desirability Bias means respondents answer according to societal expectations instead of their own beliefs. Survivorship bias involves focusing on successful outcomes while ignoring failures, misrepresenting necessary conditions for success.