##### ACHIEVE
candidates could:
- calculated probabilities or percentages using either Normal Distribution Tables or a Graphics calculator
- calculated probabilities using Probability Tree Diagrams
- calculated probabilities or proportions from the information presented in contingency
tables.
##### NOT-ACHIEVED
Candidates commonly:
- did not have any calculator (often stated by candidates) or a graphics calculator to
enable them to solve Normal Distribution problems
- were unable to solve Normal Distribution problems using the Standard Normal
Distribution table provided
- wrote probabilities to only 1 significant figure without supporting evidence (i.e. no
working)
- could not correctly add missing probabilities to a probability tree diagram
- gave their answer as a probability when a percentage was specifically asked for
- did not realise that proportions are probabilities
- could not complete a contingency table with the information provided
- did not identify the group of interest when calculating a probability from a contingency
table
- gave insufficient evidence to support their ‘relational thinking’ or ‘extended abstract
thinking’
- continued to reiterate the same point when making a comparison.
##### MERIT
In addition to Achievement, Merit candidates commonly:
- used their graphics calculator to solve more complex Normal Distribution problems (eg
inverse normal distribution problems)
- applied calculated probabilities or proportions to determine the expected value for a situation and gave the answer as a whole number for a discrete situation
- compared features of a sample distribution with a Normal Distribution by partially
describing similarities and differences using correct statistical terms
- calculated probabilities for multiple events using Probability Tree Diagrams
- could set up and solve a linear probability equation.
EXCELLENCE
In addition to
Merit, Excellence candidates commonly:
- applied their understanding of inverse Normal Probability situations to find an unknown
parameter (e.g. could find a new mean or standard deviation)
- could compare in depth the features of a sample distribution with a Normal Distribution
by fully describing similarities/differences using correct statistical terms
- found a conditional probability for an event using either an extended probability tree
diagram or conditional probability rule
- determined relative risks and drew a conclusion relating to a claim about the events
occurring. |