 In this video, I'm going to try to measure the length of a cat. This cat! As you can imagine, there will be a couple of difficulties doing so. About 41 and 62 with the queue. So after several attempts of measuring the cat, I finally got the value. I got 41 without the queue and 62 with the queue. Sorry, there is quite some discrepancy. Now the question is, what is the uncertainty of that measurement? So one way of getting the uncertainty is by estimating it. It's probably the fastest way, because you only need to take one measurement to estimate it. But it will probably produce one of the lowest precision if you do it right. So let's look how we do it. First, where does my uncertainty come from? Well, one factor is the tool I was using. This is the one everyone of you is familiar from, probably from chemistry. We take the lowest, how is the lowest increment of your tool. In my case, my meter stick had an increment of one millimeter. So an uncertainty of 0.005 meters. Next one is the object that I'm measuring. Does it have a good size, like my cat? Do I measure the whiskers? There is hair. Do I measure the hair or not? Do I measure the tail or not? So just for the tail, I think I could have been easily off, in this case, by 20 centimeters. Then, next one, the method I'm using. I was trying to run after the cat with the meter stick. Probably not the most precise way to do it. Maybe I could affect the cat or drop her or try to measure her in the sleep. So I think I could have been off probably from the method by a good one, 10 centimeters, because maybe it was still a bit curved. And then finally, the human error. Me, how good did I do the job? Was I paying a lot of attention? Did I concentrate a lot? I was mostly concentrated just running after the cat. So I think I could have maybe been off by a good five centimeters here as well. So these are all estimations, right? So don't forget these estimations. Then what I'm doing, I'm adding them up. So 5530. So I could have been off according to this estimation by a good 35.5 centimeters. Now, if I'm already having, this is an estimation. So let's not pretend the precision that we don't really have. So I'm going to run this to 0.4 meters of uncertainty. So what do I report as the final answer? Well, I'm going to take a value in between. So 0.5 plus minus 0.4 meters. And this will be the final answer for the length of my cat. 0.5 meters plus minus 0.4 uncertainty. Is that precise? No, absolutely not. This is not precise. Oh, I have an uncertainty that's almost the size of my value. What it actually means is it could be anything from 0.1 to 0.9 meters. So precision, very bad. However, accuracy. Accuracy meaning, am I absolutely sure that the real value is in there? Yeah, I'm absolutely sure. My cat is definitely longer than 10 centimeters. It's definitely shorter than 90 centimeters. Actually, probably most cats in this world will fit into this. So the currency of this measure was good. Precision was low. So the next step would then be to think, how can we improve precision? Then we go back at our estimation table and we see where did most of the uncertainty come from. Most of the uncertainty came from that we didn't define if we main cat with the queue or without the queue, with the whiskers, without the whiskers. So we can improve this uncertainty a lot. Another big contributing factor was the method. So the method here, 10 centimeters was running after the cat. Maybe I should have given her some food, measured the cat when sleeping, that would have helped. And then we looked at the ones that always are blamed, crappy equipment and human error. In this case, they were almost not significant contributors to the uncertainty. The main uncertainty came from the object itself and the method of measurement. Not from the human measuring it. And absolutely not from the probably 50 year old midget stick I was using.