| Possible Solutions | Examples of solutions in real life |
|---|---|
| Gather the data on a small scale to perform a preliminary analysis and then request additional time to complete the analysis after you have collected more data. | If you are surveying employees about what they think about a new performance and bonus plan, use a sample for a preliminary analysis. Then, ask for another 3 weeks to collect the data from all employees. |
| If there isn’t time to collect data, perform the analysis using proxy data from other datasets. | |
| This is the most common workaround. | If you are analyzing peak travel times for commuters but don’t have the data for a particular city, use the data from another city with a similar size and demographic. |
| Possible Solutions | Examples of solutions in real life |
|---|---|
| Do the analysis using proxy data along with actual data. | If you are analyzing trends for owners of golden retrievers, make your dataset larger by including the data from owners of labradors. |
| Adjust your analysis to align with the data you already have. | If you are missing data for 18- to 24-year-olds, do the analysis but note the following limitation in your report: this conclusion applies to adults 25 years and older only. |
| Possible Solutions | Examples of solutions in real life |
|---|---|
| If you have the wrong data because requirements were misunderstood, communicate the requirements again. | If you need the data for female voters and received the data for male voters, restate your needs. |
| Identify errors in the data and, if possible, correct them at the source by looking for a pattern in the errors. | If your data is in a spreadsheet and there is a conditional statement or boolean causing calculations to be wrong, change the conditional statement instead of just fixing the calculated values. |
| If you can’t correct data errors yourself, you can ignore the | |
| wrong data and go ahead with the analysis if your sample size is still large enough and ignoring the data won’t cause systematic bias. | If your dataset was translated from a different language and some of the translations don’t make sense, ignore the data with bad translation and go ahead with the analysis of the other data. |
Population: ‣
sometimes collecting information about an entire population just isn't possible. It's too time-consuming or expensive.