Histogram bin size vs. data sampling rate

1. How does bin size affect histogram?
2. How do you choose a bin size for a histogram?
3. What does bin size mean in histogram?
4. What is the relationship between file size and sample rate?

How does bin size affect histogram?

The most important parameter of a histogram is the bin width because it controls the tradeoff between presenting a picture with too much detail (“undersmoothing”) or too little detail (“oversmoothing”) with respect to the true distribution.

How do you choose a bin size for a histogram?

Choose between 5 and 20 bins. The larger the data set, the more likely you'll want a large number of bins. For example, a set of 12 data pieces might warrant 5 bins but a set of 1000 numbers will probably be more useful with 20 bins. The exact number of bins is usually a judgment call.

What does bin size mean in histogram?

A histogram displays numerical data by grouping data into "bins" of equal width. Each bin is plotted as a bar whose height corresponds to how many data points are in that bin. Bins are also sometimes called "intervals", "classes", or "buckets".

What is the relationship between file size and sample rate?

Higher sampling rate = larger file size

A higher sample rate leads to a larger file, but if the sample rate is too low, the recording will not contain enough samples to capture all of the detail of the sound.