This is the fundamental problem of data analysis: your analysis is only as good as your data. This is not an easy problem. It's hard in general to evaluate da

This is the fundamental problem of data analysis: your analysis is only as good as your data. This is not an easy problem. It's hard in general to evaluate data quality: How do we know when our data is good? Are we sure? How do we measure that and report on it? If we do have some qualitative or quantitative assessment of data quality, how do we present it in a way that is integrated with the results of our analysis? And if we want to quantitatively adjust our results for data quality, how do we do that? There are answers to the above, but they lie beyond the realm of a simple line chart, and they tend to require a fair amount of custom effort for each project. For example in the Google Ngrams case, one could present the data quality information on a chart showing the composition of data sources over time, broken out into broad categories like "academic" and "news". But then you have to assign categories to all those documents, which might be easy or hard depending on how they were obtained. And then you also have to post a link to that chart somewhere very prominently, so that people actually look at it, and maybe include some explanatory disclaimer text. That would help, but it's not going to prevent the intuitive reaction when a human looks at a time series of word usage declining. Maybe a better option is to try to quantify the uncertainty in the word usage time series and overlay that on the chart. There are well-established visualization techniques for doing this. but how do we quantify uncertainty in word usage? In this case, our count of usages is exact: the only uncertainty is uncertainty related to sampling. In order to quantify uncertainty, we must estimate how much our sample of documents deviates from all documents written at that time. It might be doable, but it doesn't sound easy. And once we have that done, will people actually interpret that uncertainty overlay correctly? Or will they just look at the line going down and ignore the rest? Your analysis is only as good as your data. This has been a fundamental problem for as long as we have been trying to analyze data, and it's never going to go away. We would do well to remember this as we move into the "AI age". It also says something about us as well: throughout our lives, we learn from data. We observe and consider and form opinions. How good it is the data that we have observed? Are our conclusions valid?