ANALYSING RESULTS &
ASSESSING VALIDITY
Analysing Data
Precision
Accuracy & Errors
Refers to how close together repeat
readings are
Increasing precision by using instruments with a higher resolution
Example: more decimal places
Refers to how close the readings
are to the true value
Increasing accuracy by repeating
the experiment several times
Systematic
errors
Fault with the equipment
(not random)
Resolve: carrying out the same experiment
using different sets of equipment
Random
errors
Unpredictable changes → cause data to differ from
the true amount by a different value each time
Can be the result of not
taking readings the same way each time
Cannot be completely
resolved since they are unpredictable
Can be reduced by increasing the
amount of data
Precision and accuracy are not related
Repeating an experiment at least twice with the aim of obtaining concordant results, which will also help discount any anomalies
Presenting Data
Drawing
graphs
Anomalies
Drawing
conclusions
Always use sharpened pencil and ruler
Use a sensible scale, sensible size of graph
Dependent variable: vertical y-axis
Independent variable: horizontal x-axis
Give the graph an appropriate title
Draw a line of best fit if possible
(straight or curved line)
Example: data points (not fit the trend of data)
result of random errors
Should always be removed from the data set
Reference data
Give examples of data, illustrate a trend or
pattern and averages
Add some explanations by using scientific knowledge
Use calculator if necessary
Validity
The extent to which the results are able
to answer the question originally asked
Reread the introduction to remind the aims
and theories before analysing results
Reducing validity
Resolution of equipment (not very
precise equipment
Size of increments (limiting the accuracy
of the measurements)
Confounding variables (affect the
dependent variable)
Error type (random and systematic errors)