When making histograms and box plots, I've seen some common mistakes that can really mess up how we understand the data. Here are some things to watch out for:

For Histograms:

1. Choosing the wrong bin sizes: This can really change how we see the data. If you use too few bins, you might miss important details. If you use too many, it can make everything look confusing. A simple way to figure out how many bins to use is Sturges’ formula:

   $k = 1 + 3.322 \log(n)$

   Here, $n$ is the number of data points you have.

2. Not scaling your axes correctly: Always make sure your axes are labeled and scaled properly. This helps everyone clearly understand what the data shows.

3. Ignoring outliers: If you focus too much on the bins, you might overlook outliers. It's important to either show them or make a note of their presence.

For Box Plots:

1. Not showing all important stats: A good box plot should include the median, quartiles, and any potential outliers. If you miss these, it can lead to misunderstandings.

2. Misunderstanding the data: Just because a box plot looks nice doesn’t mean it shows everything about the data. Keep in mind that it provides a summary, but not the complete story.

3. Lacking context: Always explain what the data means and why it's important. If you don’t, even the best-looking plot can miss the mark.