Experimental Error

Jules Bruno
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in this video, we take a look at experimental error. Now, experimental error is connected to two other concepts we've discussed before in the past. The ideas of accuracy and precision. Now we're gonna say when any calculation is done, there is a level of error involved. We're going to say error itself can be grouped into two major categories. We have our random errors and we have our systematic errors were going to say when it comes to random error, random error is basically the lack of precision. Remember, precision is how close are your results toe one another. So if you take four measurements and they're all close to one another, then you are precise. Systematic error is a lack of accuracy. Now you can have four measurements that are close to one another and therefore precise, but they may not be accurate if they're far away from the actual value. So accuracy is how close can you get to the actual agree upon value foreign object when you when it comes to measuring it. Now we're gonna say here that random errors are unpredictable and can lead to results that are either too high or too low basically, as the student with in the lab, you have basically no control over random errors. It could be the result of random occurrences that happen just in un perceived 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 g too heavy, then all of a sudden it's 2 g to 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 to hire 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 weigh an object and it's consistently 1 g 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 g to light. And it's always that no matter what object I measure, it's always 2 g less. That's a systematic error. That's something that I can basically adjust and expect to occur because I know it's gonna be 2 g to 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 little 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. Can 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 g 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. Okay, so if you're doing your experiment, you contest for your percent error and see is that 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 that 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 air formula is a useful tool for determining the precision off your calculations. Huge differences in percent error. Huge values in percent error means that your numbers are not very precise. Now we're gonna 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. The theoretical value is your known value. So let's say we're Rick. I'm weighing out an object and in the literature in your manual, it says that it should wait 25 g in the literature tells you that's the way it should be. So that is your theoretical value. You wait out a bunch of times and you get 24.8 g. That is your experimental value. You plug those in and see what your percent error is, so the numbers are pretty close to one another. It's it's safe to assume that your percent error wouldn't be that high. So it looks like would be somewhat of unacceptable value now, not always 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 123 and four, 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