No. Model-building relies on a given set of assumptions that may or may not be true. Once you've agreed to a set of assumptions, the data becomes meaningful w

No. Model-building relies on a given set of assumptions that may or may not be true. Once you've agreed to a set of assumptions, the data becomes meaningful within the framework. The framework can be bent and shaped as the assumptions evolve. If we can assume that on average, a random set of people who are randomly asked about a political topic is a reflection of the population of a whole, we can start to draw meaningful conclusions about the political topic. The OP article points to the concept of spurious correlation [1] which is a danger if you have very little domain expertise in the data that you're working with. e.g. If your regression shows US GDP is statistically significantly affected by Bangladesh butter production, you may want to discuss the results with domain experts about why the result may or may not be spurious. [1] http://en.wikipedia.org/wiki/Spurious_correlation http://en.wikipedia.org/wiki/Spurious_correlation