When trying to make sense of data, it's easy to fall victim to confirmation bias, irrelevancy, statistical insignificance, and causation vs. correlation and action vs. intent confusion. Here are solutions to these (and more) common analysis problems.

Summarized...

1. Confirmation Bias: You have a hypothesis in mind but you are only seeking data patterns that support it – ignoring all data points that reject it.

2. Irrelevancy and Distraction: Focusing on data that is irrelevant to the problem you are trying to solve or being distracted by data that isn't directly connected to your analysis goal. In the age of Big Data, this is doomed to happen more and more.

3. Causation vs. Correlation: Mixing the cause of a phenomenon with correlation. If one action causes another, then they are most certainly correlated. But just because two things occur together doesn't mean that one caused the other, even if it seems to make sense.

4. Statistical Significance: Using data sets that are too small to suggest a trend or comparing results that are not different enough to have statistical significance.

5. Action vs. Intent: Inferring the wrong intention based on the actions recorded in the data rather than the suggested intent.

6. Apples and oranges: Comparing unrelated data sets or data points and inferring relationships or similarities.

7. Poor data hygiene: Analyzing incomplete or "dirty" data sets and making decisions based on the analysis of that data.

8. Narrow focus/not enough data: Analyzing data sets without considering other data points that might be crucial for the analysis (for example, analyzing email click-through rate but ignoring the unsubscribe rate).

9. Bucketing: The act of grouping data points together and treating them as one. For example, looking at visits to your website and treating unique visits and total visits as one, inflating the actual number of visitors but understating your true conversion rate.

10. Simple mistakes and oversight: "It happens to the best of us."