Like many ads on the web, Google Consumer Surveys reports on the inferred age, gender, and parental status of anonymous respondents based on the websites users visit and location based on IP addresses. Income, and urban density are then approximated using census data for particular geographic regions. To see what inferences are associated with your browser, visit google.com/ads/preferences.
Please note that it’s possible that we may miscategorize people. For example, if someone visits websites that are usually frequented by younger people, they may be categorized as younger than their actual age. Similarly, if a household uses a shared computer, we may categorize that “user” based on the combined interests of the household.
Consumer Surveys weights results by inferred gender, age and geography when possible to make the sample as representative as possible of the internet population. If you prefer to see unweighted data, you can turn off weighting to see raw results.
Please note that we don't reweight multi-select question type as it will have consequences on the follow up questions.
Root Mean Square Error (RMSE)
The RMSE measures the differences between the desired distribution and actual distribution for each targeted population segment and calculates a weighted average error. The RMSE technique weights large errors more than small errors. Thus, if the difference in one segment is very large, it would have a greater effect on the RMSE than if there were small errors across several segments. The lower the RMSE score, the closer we are to representing the US Internet population.
Note: The table provided on the survey results page communicates the sampling bias for three segments: gender, age and region. However, our targeted population segments also includes combinations of gender, age, and region. Combinations can include any two or three of these segments.
In the results report, the values inside the parenthesis are the ranges to determine the confidence interval and the graphical illustration of it is called Error bar as previously discussed. If you run the same survey again, there's a 95% chance that the percentage of respondents who chose that answer will be in that confidence interval range.
E.g. Answer A 33.5% (+9.9/-8.7)
There's a 95% chance that the percentage of respondents who choose this Answer A will be between 43.4% and 24.8% if you run the survey again. Because 33.5 + 9.9 = 43.4 and 33.5 - 8.7 = 24.8.
Please note that when targeting the General Population, the sampling bias table will not show up unless there are at least 250 responses.