If we present an error (z.B. 5cm) with a measurement (z.B. 180 cm), this does not mean error, but due to experimental constraints, there is an uncertainty of 5cm in the indicated value. Accuracy is the proximity of a measure to fair value for this measurement. The accuracy of a measurement system refers to the proximity of the concordance between repeated measurements (repeated under the same conditions). Measurements can be both accurate and precise, accurate, but not precise, accurate, but not accurate, but not accurate or not. A number of measurements can be described as accurate when the area is very small, i.e. the area near 0 A is described as imprecise when the area is wide, i.e. the range is not close to 0. The reproducibility of a measurement is called accuracy. If all the measurements are very similar, we say that the determined value is known. If we cannot get similar measures, we cannot say that the value is known, but we say that the measurements are imprecise.

A set value is accurate if the percentage of relative error is low, close to 0%. The percentage of the relative error in the student`s results is NOT close to 0%. The student`s temperature values are NOT accurate, they are imprecise. PRUDENCE NOTE — Avoid excessive units created by calculators and tables. In the example above, we measured 50 plots and I found that there were 4.5 -0.5 plants/plot. In fact, when I finished the calculations in my calculation table, there were 4,423 plants per plot. But I made the informed decision to round to the next semi-plant (0.5 plant). The number 4.423 assumes that I was able to detect fractions of a plant at 1/1000 (or 0.001). I`m sorry, I just can`t imagine 1/1000th of a plant, and I don`t think it`s very useful to try to make decisions based on 1000ths of a plant.

Especially because I only measured 50 plots. Just because you can calculate numbers with a lot of units beyond the decimal number doesn`t mean you should. Here are some guidelines: the more measures there are, the closer we get to knowing the true value of a quantity. For several measurements (replications), we can evaluate the accuracy of the results, and then use simple statistics to estimate how close the average value would be to the actual value if there was no systematic error in the system.