Outliers can really affect how we understand data and make conclusions. Here’s how they do this:
Skewed Estimates: Outliers can change the average (mean) a lot. For example, if most people in a group earn around £30,000 but one person earns £1,000,000, the average income looks higher than it really is. This can give a wrong picture of what’s normal.
Wider Confidence Intervals: Outliers can make the spread of numbers (standard deviation) larger. This means that when we try to estimate something, our guess can be less accurate. We end up with a wider range of possible outcomes.
Impact on Hypothesis Tests: When we run tests to make decisions, outliers can change the p-values. This can lead us to make mistakes about what is actually important in the data.
In short, outliers can really twist our understanding of the data. That’s why it's super important to find them and deal with them properly to make sure our conclusions are strong and reliable.
Outliers can really affect how we understand data and make conclusions. Here’s how they do this:
Skewed Estimates: Outliers can change the average (mean) a lot. For example, if most people in a group earn around £30,000 but one person earns £1,000,000, the average income looks higher than it really is. This can give a wrong picture of what’s normal.
Wider Confidence Intervals: Outliers can make the spread of numbers (standard deviation) larger. This means that when we try to estimate something, our guess can be less accurate. We end up with a wider range of possible outcomes.
Impact on Hypothesis Tests: When we run tests to make decisions, outliers can change the p-values. This can lead us to make mistakes about what is actually important in the data.
In short, outliers can really twist our understanding of the data. That’s why it's super important to find them and deal with them properly to make sure our conclusions are strong and reliable.