![]() ![]() Perhaps you used a “literature value” rather than measuring that quantity under your own experimental conditions. Perhaps you further assume that physical constants, like equilibrium constants, enthalpies, and others, do not change (much) from 25.0° C to the actual ambient temperature. Maybe you assumed a typical ambient air pressure without taking a measurement of the actual value. Perhaps you assume that the room temperature is 25.0° C (most UCalgary building HVAC is set to 21 ° C and fluctuates around that). Some Common Sources of ErrorĮvery experiment is different, but if you are analyzing your procedure for potential sources of uncertainty, there are a few places you can start: Assumptions About Physical StatusĮvery procedure comes with some assumptions. ![]() In a group of people observing the same meniscus you expect to get a range of readings, mostly between 25.5 – 25.7 mL. Another example is the interpolation of the final digit on a scale, as in the example earlier in this section. You can observe random error when you weigh an object (say, recording a mass of 1.0254 g) and when re-weighing it, you get a slightly different measurement (say 1.0255 g). Random error can usually be reduced by adjusting the procedure or increasing skill of the experimenter, but can never be completely eliminated. Random errors are those that primarily affect the precision of your final value. Another could be doing calculations using an equilibrium constant derived for a temperature of 25.0° C when the experiment was done at 20.0° C. One example of a systematic error could be using a pH meter that is incorrectly calibrated, so that it reads 6.10 when immersed in a pH 6.00 buffer. These errors usually exist and are often constant for the duration of the experiment – or if changing slightly, like an instrument reading “drifting” with time, they are in a consistent direction (higher or lower than the “true” value). These can often be greatly reduced or eliminated entirely by adjusting your procedure. Systematic errors are those that affect the accuracy of your final value. While these are not sources of error, knowing the two main ways we classify uncertainty or error in a measurement may help you when considering your own experiments. When considering sources of error for a lab report be sure to consult with your lab manual and/or TA, as each course has different expectations on what types of error or uncertainty sources are expected to be discussed. Each experiment and measurement needs to be considered carefully to identify potential limitations or tricky procedural spots. When taking a measurement or performing an experiment, there are many ways in which uncertainty can appear, even if the procedure is performed exactly as indicated. ![]()
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