- using sample and differences, statistics to make recognising the relevance multiple point estimates of of the central limit displays population parameters theorem - making - recognising the effect - using methods such as informal of sample size on the re-sampling or inferences variability of an randomisation to assess estimate the strength of evidence populations · Evaluate · Evaluate a wide from sample statistically based range of statistically data reports: based reports, - justifying - interpreting risk and including surveys findings, relative risk and polls, using - identifying sampling experiments, and displays and and possible observational measures errors in studies: · Evaluate surveys, including - critiquing statistical reports polls claims in the media by · Investigate - interpreting margins of relating the situations that error displays, involve elements · Investigate situations statistics, of chance: that involve elements processes, and - comparing theoretical of chance: probabilities continuous - calculating probabilities of used to the distributions, such as independent, combined, claims made the normal and conditional events · Investigate distribution, with - calculating and situations that experimental interpreting expected involve elements distributions values and standard of chance: - calculating deviations of discrete - comparing probabilities, using random variables discrete such tools as two-way - applying distributions theoretical tables, tree diagrams, such as the Poisson, distributions simulations, and binomial, and normal and technology experimental
multiple displays - making informal inferences populations from sample data - justifying findings, using displays and measures · Evaluate statistical reports in the media by relating the displays, statistics, processes, and probabilities used to the claims made · Investigate situations that involve elements of chance: - comparing discrete theoretical distributions and experimental - using sample statistics to make point estimates of population parameters - recognising the effect of sample size on the variability of an estimate · Evaluate statistically based reports: - interpreting risk and relative risk - identifying sampling and possible errors in surveys, including polls · Investigate situations that involve elements of chance: - comparing theoretical continuous distributions, such as the normal distribution, with experimental distributions - calculating probabilities, using such tools as two-way tables, tree diagrams, simulations, and technology and differences, recognising the relevance of the central limit theorem - using methods such as re-sampling or randomisation to assess the strength of evidence · Evaluate a wide range of statistically based reports, including surveys and polls, experiments, and observational studies: - critiquing claims - interpreting margins of error · Investigate situations that involve elements of chance: - calculating probabilities of independent, combined, and conditional events - calculating and interpreting expected values and standard deviations of discrete random variables - applying distributions such as the Poisson, binomial, and normal