Sometimes, in biological experiments, it can be difficult to spot anomalous results. Using a scatter graph with a line of best fit is usually the most effective way. You can quickly see any pattern and spot if any of the points are a long way from it. These can then be identified as anomalous. There are many possible explanations for obtaining anomalous results. Human errors can lead to data which is anomalous and a lack of precision whilst taking measurements is one possible explanation. Using inappropriate measuring equipment could create problems too. If a linear scale is marked only in... Show more Sometimes, in biological experiments, it can be difficult to spot anomalous results. Using a scatter graph with a line of best fit is usually the most effective way. You can quickly see any pattern and spot if any of the points are a long way from it. These can then be identified as anomalous. There are many possible explanations for obtaining anomalous results. Human errors can lead to data which is anomalous and a lack of precision whilst taking measurements is one possible explanation. Using inappropriate measuring equipment could create problems too. If a linear scale is marked only in cm, it will not be as accurate as one marked in mm because you will have to estimate any readings that fall in between the cm marks. If the equipment is not adjusted to zero correctly before starting the experiment, this can give rise to zero errors which can lead to anomalous data. But it's not just human errors that you need to consider - faulty equipment or even the method of the experiment could be responsible. Tip: When discussing anomalous results in an evaluation, be very specific when suggesting causes. Say things such as 'the result at 25oC is higher than expected so the temperature might have gone up when I was taking the reading'. Show them you are using your general scientific knowledge to find reasons for the anomaly - you know that reactions work faster at higher temperatures. Show less
Sometimes, in biological experiments, it can be difficult to spot anomalous results. Using a scatter graph with a line of best fit is usually the most effective way. You can quickly see any pattern and spot if any of the points are a long way from it. These can then be identified as anomalous.
There are many possible explanations for obtaining anomalous results. Human errors can lead to data which is anomalous and a lack of precision whilst taking measurements is one possible explanation. Using inappropriate measuring equipment could create problems too. If a linear scale is marked only in cm, it will not be as accurate as one marked in mm because you will have to estimate any readings that fall in between the cm marks. If the equipment is not adjusted to zero correctly before starting the experiment, this can give rise to zero errors which can lead to anomalous data. But it's not just human errors that you need to consider - faulty equipment or even the method of the experiment could be responsible.
Tip: When discussing anomalous results in an evaluation, be very specific when suggesting causes. Say things such as 'the result at 25oC is higher than expected so the temperature might have gone up when I was taking the reading'. Show them you are using your general scientific knowledge to find reasons for the anomaly - you know that reactions work faster at higher temperatures.
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