In this video, we take a look at experimental error. Now experimental error is connected to 2 other concepts we've discussed before in the past, the ideas of accuracy and precision. Now, we're going to say when any calculation is done, there is a level of error involved. We're going to say error itself can be grouped into 2 major categories. We have our random errors and we have our systematic errors. We're going to say when it comes to random error, random error is basically the lack of precision. And remember precision is how close your results are to one another. So if you take 4 measurements and they're all close to one another, then you are precise. Systematic error is a lack of accuracy. Now you can have 4 measurements that are close to one another and therefore are precise, but they may not be accurate if they're far away from the actual value. So accuracy is how close you can get to the actual agreed-upon value for an object when it comes to measuring it. Now we're going to say here that random errors are unpredictable and can lead to results that are either too high or too low. Basically, as the student within the lab, you have basically no control over random errors. It could be the result of random occurrences that happen just in unperceived ways. So it's really hard to minimize these types of errors. Now when we say it could be either too high or too low that means that if I, let's say, measure out an object and it is 1 gram too heavy, then all of a sudden it's 2 grams too light. And it fluctuates back and forth between being either too heavy or too light in terms of its mass. This would be an example of random error. Now here's the thing. Because our results can either be too high or too low, the best way to approach random error is to take several measurements over time. If you take several measurements and then average out all those measurements that will help to reduce your random error. Systematic error is different. Systematic errors are more predictable and can lead to results that are always too high or always too low, but not both. So let's say I weigh an object and it's consistently 1 gram too heavy. That is a systematic error. The error is always too high. Or I use another scale and I measure out something and it's consistently 2 grams too light. And it's always that. No matter what object I measure, it's always 2 grams less. That's a systematic error. That's something that I can basically, adjust and expect to occur because I know it's going to be 2 grams too light each time. But here's the thing. Whereas random errors are easier to spot because you'll get measurements that are too high and too low randomly, systematic errors are harder to spot. You may not know that you have a systematic error because let's say you take that object and you measure it several times and it keeps giving you that same exact value. You think that you are accurate. But in reality, the scale is giving you numbers that are 2 grams less than what they should be. You wouldn't know that. Now in most cases, a percent error of less than 10% will be acceptable. So if you're doing your experiment, you can test for your percent error and see is it within these guidelines. If so, it will be an acceptable answer. Percent error itself is experimental value minus theoretical value in absolute brackets which means that even if you get a number that's negative inside of here, because it has absolute brackets it'll be a positive value. And then it's divided by your theoretical value and you multiply it times 100. Now remember this times 100 is what gives us our percentage value, similar to mass percent and other concepts that we've talked about in other chapters. We're going to say the percent error formula is a useful tool for determining the precision of your calculations. Huge differences in percent error, huge values in percent error means that your numbers are not very precise. Now we're going to say here that the experimental value is your calculated value. You do the experiment, you crunch some numbers, you do the math, you get this total. That's your experimental value. You say, "I'm weighing out an object." And in the literature, in your manual, it says that it should weigh 25 grams. In the literature, it tells you that's the weight it should be so that is your theoretical value. You weigh it out a bunch of times and you get 24.8 grams. That is your experimental value. You'd plug those in and see what your percent error is. Since the numbers are pretty close to one another, it's safe to assume that your percent error wouldn't be that high. So it looks like it would be somewhat of an acceptable value. Now now that we've seen the different classifications of error, let's see if you guys can tackle these example questions. Attempt to do the first one on your own, but if you get stuck, don't worry. Just come back and take a look at my explanation on the best answer here. Here, I've done it as 1, 2, 3, and 4, meaning that more than one answer could be the correct choice. So pay attention to what we talked about up above to answer this question on random errors.