Episode 4, Exercise 1 Histogram and QC

### Brief description

1. Introduce the concept of histograms as measuring empirical distributions, i.e. how often does a varible have a certain value. Use repeated dice rolling as an example:
- start with 10 manually set dice roll results and use matplotlib to compute and plot the histogram.
- use generated dice rolls (from np.random) to show that histograms visualize the quality of the dice

2. Extend the concept of from dice rolls to pixel intensity values.
- Plot histogram of one 2D uint8 image with 256 bins (as before with the dice rolls)
- Introduce the concept of _binning_ in histograms.
- What are good bin sizes? I. e. histogram is neither too sparse nor too congested

3. Explore image histograms
- Display various example images with their histogram next to it.
- Observe and discuss different properties and how they relate to the image (quality), e.g. modes, dim-signal, saturation, sparcity, etc

### Learning objective(s)
* In 1. to 2. the Learners use and explore arguments of the `plt.hist` function. [Learning Objective](https://github.com/carpentries-incubator/bioimage-analysis-python/wiki/Learning-Objectives#histogram-and-quality-control) (perhps also `np.histogram`)
* In 3. the second learning objective (what does a histogram tell us about an image and its quality) is adressed

### Volunteer(s)
@sommerc

No source file or test is named; start by locating the Episode 4 lesson and matching its existing exercise structure. Done means covering manual and generated dice rolls, pixel-intensity histograms with binning, and image examples that connect histogram properties to image quality using matplotlib and NumPy.

Written by the indexing model from the issue text.