When working to answer a research question or add support to our intuition, it is necessary to assess the quality of our data. Poor data often can be worse than no data, leading to flawed interpretations and misinformed decisions. An important disclaimer here is that most data has flaws and thus limitations. The key is to consider the data in the context of our question (hence why critiquing our assumptions and understanding the full picture of the broader problem is so important) while also weighing the flaws. By doing this, we are able to better anticipate the utility and limitations of the conclusions we draw from the data. In order to evaluate data quality, consider the following characteristics…