Suppose you want to collect data on how often people in your community use their new blue bin for recycling. You decide to ask the first 100 people you meet at an Enviroment Day even.

how might your res
be biased?

Of course they'll be biased!

People who don't recycle are not likely to attend an Enviroment Day event.

ok thanks

what bias might you expect from this sample?

The results of your data collection may be biased in a few ways:

1. Selection bias: By asking people at an Environment Day event, you are more likely to encounter individuals who are already interested in and actively participating in recycling. This could result in an overestimate of the usage of the blue bin in the community, as those who do not attend the event may have different recycling behaviors.

2. Volunteer bias: People who are willing to participate in surveys or interviews may have different characteristics or behaviors compared to those who decline to participate. This could lead to an overrepresentation of individuals who are already motivated to recycle, potentially inflating the reported usage of the blue bin.

3. Social desirability bias: When asked about their recycling behavior, people may be inclined to provide socially desirable responses, such as exaggerating their recycling habits to appear more environmentally conscious. This bias can lead to an overestimate of the recycling rate.

To mitigate these biases and obtain more reliable data, it is important to use random sampling techniques. Instead of approaching the first 100 people you meet, consider using a random sampling method like drawing names from a hat or selecting a random sample from a comprehensive list of community members. This ensures that all individuals in the community have an equal chance of being included in your study, reducing the risk of bias.