The key is not to get discouraged by conflicting data but to see it as an opportunity to learn and verify more deeply. Take a step back and look at where the data is coming from. Are the sources reliable? If you’re still stuck, try cross-checking with more data. Don’t underestimate the power of collaboration. A fresh set of eyes often sees things more clearly.

Well, indeed checking for data quality and potential extreme outliers if they may be driving and greatly influencing your results are extremely important. You need to know your data abit before conducting more sophisticated analyses. Validating your results in various ways would be the second step, there are various ways to do that without employing other data sources, but if one has access to other data sources, by all means use it to double check your conclusions. If not, you need to understand the effect you captured, does it make sense theoretically or is there any model supporting this conclusion, is there contradictions across your conclusions, also check what other analyses claim to have found.

When you find conflicting data in your analysis, you need to first understand how the tool for data collecting is constructed and how it works in its core. You need to know at what time it collects the data, the frequency of the collected data, and how data gets presented for analysis. It can be that the code for data collecting is bugged, it can be that power shortage influenced the data collection intervals. If you find it is none of this, then it could be human error in data transmission of intentional data modifications to hide something, augment something or diminish something else. First you need to find out what caused the data discrepancy and then you can decide on further steps to take. You can fix, prevent, or make new protocols.

Conflicting data can be frustrating, but I approach it methodically. I verify the reliability of sources and cross-check findings with additional data. If discrepancies persist, I consult with colleagues or domain experts for fresh perspectives. This process not only helps resolve conflicts but also strengthens the integrity of the analysis.

Data within the same study may conflict for various reasons. Note that one cannot overlook a conflict. It may show an error- e.g. in the original measurement instrument, data entry or coding. Check the result against a reliable external source, and discuss the result with knowledgeable others. Or it may provide a new perspective on the data and provide the beginnings of a new theory that we would like to pursue. Often conflict in data gives us deeper insight and opens new opportunities - as long as we're not pursuing an error in the research.