What you just saw, if everything worked correctly, was 225 random sequences being generated on the right. As each was generated it was compared to CATTAG. The resulting score was then used to push up the gold rod by one increment over the relevant score. After all 225 sequences have been created and compared, a new histogram of gold rods overlays the grooves. The increments are adjusted so that if the experimental frequencies occur with exactly the theoretical percentages, then each gold rod will exactly fill the corresponding groove.
But that is very unlikely. I can not say precisely what you're seeing, as each time you reset the panel and push another buttom the results will be different. But it is highly likely that some of your gold rods go over the top of their corresponding grooves, and some fall short. The basic shape of the experimental distribution (gold rods) should look similar to the theoretical distribution (grooves), but it won't be exact.
The shapes are similar because mathematics forces them to be; the theoretical distribution is an ideal - not unattainable, but unlikely. That's because the sample size of 225 is just too small. Each gold rod that goes one increment over the theoretical distribution groove means there's another rod that must be one increment under. With bigger increments, you get greater variation from the theoretical distribution. And the increment size increases as the sample size decreases.
Let's test this. Reset the panel, then push the button for a sample size of 15 (choices are 15, 75, 135, 225, and 675). Do it a few times. What you should be observing is that the experimental distribution is now having much more trouble trying to fit the theoretical distribution. Ok, try the 75 button a couple of times, then work through all the higher buttons. You should observe that as the sample size increases, the experimental distribution has less trouble conforming to our theoretical expectations. If we were to use an infinite sample size - the entire population of every such random sequence that could ever be generated (whatever that means) - then the experimental and theoretical percentages would be identical. That's the difference between populations and samples, and that's part of the reason we need statistics. Very often an entire population of experimental results is unattainable - or even lacking in meaning. But we can approximate a population with a sample of experimental results, and if the sample is big enough, and chosen carefully enough, it can tell us something about the theoretical population, although with some uncertainty. As we have seen, this uncertainty decreases as the sample size increases. Ah, the wonders of statistics. Anyway, play around a bit with the panel at right. I'm going to go get a cup of coffee. See you on the next page.