Checking whether the data you collected and will work with represents a population as a whole depends on the specific situation. 1) If you take a subsample of a larger dataset (e.g. randomly selecting observations or observations having certain features), you can quickly check whether the means and variances of all variables align. More rigorously, you could create histograms or perform two-sample t-tests of the difference in means (likely with unequal variances, can be tested with an F-test). 2) If you collect data (e.g. survey respondents) from a entire population, pay close attention to randomly selecting participants and addressing possible biases in non-responses (i.e. if certain characteristics make it more likely to not respond)

Curious if your data reflects the full picture? It all begins with ensuring your sample accurately represents your audience. Here's what I've learned to keep in mind: 1) To ensure your sample represents the right people, clearly define who you want to understand. 2) Then, pick the right sampling method. Random sampling gives everyone a fair shot, while stratified sampling ensures all key groups are included. 3) Avoid bias by not just going for what's convenient. Make sure your sample is big enough to show variety, but not too big that it’s overwhelming. 4) Be mindful of non-response bias—if some people don’t participate, it can affect your results. Test things out on a small scale first to spot any issues.

Employ stratified random sampling to ensure all relevant subgroups are proportionally included, mirroring the diversity of your population. Conduct power analysis to determine the appropriate sample size that balances statistical significance with resource constraints. Regularly assess your sample for potential biases, such as selection bias or non-response bias, and implement strategies to mitigate them. Utilize statistical tests like chi-square or t-tests to compare your sample's demographics with known population parameters. Consider oversampling underrepresented groups to improve representativeness, but be sure to adjust for this in your final analysis.