Interpreting inferential statistics can be tricky for data scientists. Here are some of the main challenges they face:

1. Complex Models: Some statistical models are hard to understand. This can lead to mistakes in figuring out the results.

2. Assumptions: Inferential methods depend on certain rules, like normality and independence. If these rules aren’t met, the conclusions can be wrong.

3. Sample Bias: If the sample (the group of data chosen for the study) isn’t representative of the whole population, the results can be misleading. This makes it hard to apply the findings to a larger group.

4. Significance vs. Practicality: A p-value (like $p < 0.05$) shows that the results are statistically significant. But that doesn’t mean they are important or relevant in real life.

To deal with these challenges, data scientists should focus on getting strong training. They should also run thorough checks to make sure their methods are sound. Lastly, they need to look at both the statistical significance and how the results apply in the real world.