CE – Get it Right!

I have already made some blog postings regarding the first two assessed IA criteria, namely ‘Desgin’ labs and ‘DCP’ labs. This posting will focus on the third assessed criteria, ‘Conclusion and Evaluation’ or ‘CE’ or ‘CEv’.

As with the other two assessed criteria, CEv is split into three aspects, aspects 1, 2 and 3. We will focus on each aspect individually.

Aspect 1 – it is the opinion of the author that this aspect is the hardest aspect of all to achieve the ‘c’. To begin with, this aspect is really split up into three sub aspects. Secondly, to stand a good chance of achieving ‘c’ for this aspect, there also needs to be some good error analysis in DCP aspect 3. Sometimes, if DCP aspect 3 is not so good, it will affect CE aspect 1.

So, the first sub aspect deals with writing a relevant conclusion. That’s the easy part. It also involves calculating a percentage error. To do this, the theoretical (literature) value must be taken away from the calculated value and expressed as a percentage by dividing this number by the theoretical value and multiplying by 100:

It is easier to write it as:

([Calculated value – Theoretical value] / Theoretical value ) x 100

For example, let’s use an arbitrary value of ‘50’ as our calculated value. The literature value is ‘75’. This means our percentage error is:

([50 – 75) / 75) x 100 = 33%*

* We ignore the ‘-‘ value

This calculation also hits the second sub aspect as it needs to literature value (and quoting the literature value is what the second sub aspect requires). Don’t forget to fully source where you obtained the literature value from.

The third sub aspect comes from commenting on the calculated random error (from DCP aspect 3) with respect to the literature value.

In order to do this, a little note on errors or uncertainties.

Firstly, there are types, random (which can be measured) and systematic that cannot be measured. So the uncertainty in reading a temperature is random (as you measure it from the scale) but heat loss, or purity of reagent (eg, hydrogen peroxide that has slowly decomposed)  is a systematic error as it cannot be measured.

So if the random error was 5% and using our value of 50 from above, we could conclude that our unmeasured error (systematic) was larger than our random error (measured error) as 50 is more than 5% off 75.

However, if the random error was found out to be 50%, we could conclude that we had measured most of the errors as 50 is just about within 50% of 75.

CEv gets easier once aspect 1 is dealt with.

Aspect 2 deals with flaws or faults in the method. That said, do not go blindly writing a list of flaws, they must be in the same direction as the error. So, if you were trying to calculate the concentration of KMnO₄ using H₂O₂ and you calculated the concentration of KMnO₄ to be 0.100 mol dm ^-3 but the literature value told you that it was 0.0500 mol dm^-3 it would be incorrect to say that a flaw in the method was that the H₂O₂ may had decomposed. Your value is higher than the accepted value but if the H₂O₂ has decomposed it would give you a calculated value smaller than the literature value.

Aspect 3 is the easiest aspect to get the ‘c’ and it relies on you being able to come up with some realistic ways of improving the flaws you identified in aspect 2.

It is sometimes a good idea to write aspect 2 & 3 in table form, one heading being ‘Experimental faults’, the other being ‘Improvements to the faults’.

I appreciate aspect 1 and in some ways, aspect 2 can be tricky to get your head around so if you do have any further questions, please feel free to post them here. I look forward to reading them!