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So, what does this mean for us, as experimental scientists? If we take many repeated measurements, we can estimate the mean from the normal distribution by calculating the average value of our measurements. Likewise, we can calculate the standard deviation (SD) of those measurements. While you could do these calculations by hand, it is much easier to use a spreadsheet such as Microsoft Excel or Google Sheets. Here's an example: A B C D E F G H | J 1 Time 1 Time 2 Time 3 Time 4 Time 5 Time 6 Time 7 Time 8 |Average Time| SD Time 2 5.2 4.7 5.1 4.6 4.9 5.5 5.2 4.8 5 0.30237158 The equation in cell 12 that calculates the average is =AVERAGE (a2:h2) and the equation in cell J2 that calculates the standard deviation is =STDEV (a2:h2) These equations work the same in Excel or Sheets. Therefore, we have a good way to calculate the range of values that we could record for any given measurement. From the data on the above spreadsheet, we could say that, on average, about 68% of our measurements should be between 4.7 and 5.3. However, that's not exactly what we mean by error, which is how certain we are about our measurement. Imagine that we take 8 measurements and find that the average time is 5 seconds. If we then go back and take 100 more measurements, and the average is still 5 seconds, we will be more certain that 5 seconds is a reliable measurement for the time. However, the standard deviation doesn't capture this. Instead, we need to use the standard error (SE), which is given by SE = o/N - Page 10 -
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