A significant challenge with secondary data is the potential for misinterpretation, as you weren't present for its original collection. Without thoroughly understanding the data collection methods and context, you risk drawing flawed conclusions. For example, a variable like "Household Income" might appear straightforward but, upon checking the codebook, could have a much narrower or specific definition (e.g., only taxable income for the primary earner). Such discrepancies between your assumptions and the original operational definitions or measurement context can drastically skew your findings. Always engage diligently with documentation like codebooks to avoid misinterpreting the data and to maintain your research's validity.