Communicating statistics to people who are not experts can be tricky. Here are some common challenges:
Hard Words: Researchers often use complicated terms like "p-value," "confidence interval," and "effect size." These words can confuse people who aren’t familiar with statistics.
Wrong Understandings: The idea of statistical significance can be confusing. For example, just because a result has a does not mean it’s important in real life. This can lead to wrong conclusions.
Lack of Context: People who aren’t statisticians may not understand the bigger picture. They might not see how the results of a study can affect everyday life.
To make things clearer, researchers should use simpler words. They can also use charts and graphs to show their results. It’s important to highlight how the findings matter in real-world situations. Having open conversations can also help reduce confusion.
Communicating statistics to people who are not experts can be tricky. Here are some common challenges:
Hard Words: Researchers often use complicated terms like "p-value," "confidence interval," and "effect size." These words can confuse people who aren’t familiar with statistics.
Wrong Understandings: The idea of statistical significance can be confusing. For example, just because a result has a does not mean it’s important in real life. This can lead to wrong conclusions.
Lack of Context: People who aren’t statisticians may not understand the bigger picture. They might not see how the results of a study can affect everyday life.
To make things clearer, researchers should use simpler words. They can also use charts and graphs to show their results. It’s important to highlight how the findings matter in real-world situations. Having open conversations can also help reduce confusion.