Mathematics: Applications and Interpretation HL
Advanced inference and statistical decisions
Standardisation, p-values, errors, two-sample inference, paired tests, and interval interpretation for AI HL.
Standardise normal variables
Convert a normally distributed value to a z-score using mean and standard deviation.
Interpret p-values from test statistics
Use a test statistic and significance level to make a contextual decision.
Interpret Type I and Type II errors
Identify the contextual meaning of decision errors in hypothesis tests.
Use two-sample mean tests
Standardise a difference between two sample means and interpret the test statistic.
Use paired data tests
Use paired differences to calculate a test statistic for matched observations.
Interpret confidence interval decisions
Use a confidence interval to decide whether a hypothesised value is plausible.
Calculate expected frequency
Calculate an expected cell count for a chi-square test.
Use chi-square goodness of fit
Calculate a chi-square contribution or statistic from observed and expected counts.
Interpret confidence intervals for proportions
Connect a proportion confidence interval to a contextual claim.
Use one-proportion z tests
Calculate a one-proportion z statistic and interpret its sign.
Calculate pooled proportions
Pool two sample proportions for a two-proportion z test.
Use two-proportion z statistics
Calculate the z statistic for a difference between two proportions.