 It's an inescapable fact of life that there will always be some uncertainty in any measurement you make and Whenever you do an experiment, there will be some errors introduced that mean the value you measure is not the true value of the object Now these errors are broadly classified into random and systematic errors and Good experimental design will attempt to minimize these as much as possible and get results that are as precise and accurate as possible Now we're going to go through and explain all of these terms in a bit more detail Random errors are due to things that change a bit each time you make the measurement Whereas systematic errors remain the same each time you repeat an experiment So let's go back to my system for measuring someone's height and think about what sort of errors might come into this experiment First of all the person being measured might not stand the same way each time And then the person doing the measurement might not balance the ruler perfectly horizontal Or maybe they hold the measuring tape not quite vertical or even not flush against the floor So these things are all examples of random errors Now what about systematic errors? Maybe the measuring tape has had its end bent off so that the counting starts at half a centimeter instead of zero Or maybe it's an old plastic measuring tape that's been stretched out of shape and the centimeter marks are no longer really one centimeter long Random errors tend to fluctuate around the true value So one way to reduce random error is to repeat a measurement many times and then take an average of those values The many small random errors will cancel out overall Systematic errors on the other hand can sometimes be difficult to identify One approach is to use different measuring instruments Or you can check your measuring instrument against a known object and this process is called calibration