Selection Bias

Selection bias happens when the group of people or data you choose for a study doesn't accurately represent the whole population you're interested in.

For example, suppose a researcher wants to find out the average amount of time people in a certain city spend exercising each week. If they only survey people at a gym, they're likely to get answers from individuals who exercise more than the average person. This means the study will overestimate how much the general population exercises because it didn't include people who don't go to the gym.

In data analysis, selection bias can lead to incorrect conclusions because the data doesn't reflect the true situation. If decisions are made based on this biased data, they might not be effective or could even be harmful. To avoid selection bias, it's important to use random sampling methods and make sure all groups within the population have an equal chance of being included in the study. This helps ensure that the findings are accurate and can be applied to the whole population.

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