The bin size in an FFT is not a setting, but is a result of the interplay between the number of data points and the data-collection rate.

The number of bins in the FFT scales with the number of data points collected, with an additional power-of-two point number requirement. The FFT algorithm requires 2^n points, where n is an integer. If the number of points collected is not a power of two, the FFT algorithm either has to pad the data or truncate it. Logger Pro does both in various situations.

The number of bins increases as you supply more data points. The number of bins is close to an integer power of two; if a data table would display 1024 points, but the table cuts off at 1000, the number of bins will be 1000. If the data table has 1100 points, you’ll see 1024 bins. If you supply 2500 points, you’ll see the next power of two in bins, or 2048.

In addition, the maximum frequency of the FFT is half the sample rate.

These two factors determine the bin size. It is not a setting, but a result of the FFT algorithm.

We often set up an experiment that has two graph windows. One has a short time interval on the horizontal axis and displays the waveform nicely, and the other has a long time interval on the horizontal axis. The time of the longest duration graph controls the data collection so we get the small bin size we are looking for.

What is the best procedure to get a clean sine wave when using a tuning fork with the microphone?