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This is a session of Topic 3: Statistics education at the post-secondary level             Full topic list

(Thursday 6th, 14:00-15:30)

Teaching nonparametric methods



Many methods of statistical inference, such as hypothesis testing, rely on the assumption that the observations come from a distribution of known form, apart from the value of some parameter(s). A well known example is the common assumption of an underlying normal distribution. This model assumption is not always justifiable and checking it can be problematic, especially in small samples of data. Statistical methods which are valid without these full distributional restrictions are called distribution-free or nonparametric. They work under much weaker assumptions on the underlying distribution, such as continuity or symmetry. These methods are typically based on the ranks of the observations and they often turn out to be conceptually simple.

The ever growing power of computers greatly influenced the area of nonparametric statistics. The so called nonparametric smoothing techniques became invaluable instruments in practical data analysis. These include estimation of density functions, distribution functions, regression curves and other curves. The speakers in this session will cover several interesting aspects of the field of nonparametrics and they will illustrate how these could fit in the teaching of post secondary statistics courses.


PaperTitlePresenter(s) / Author(s)
3F1Smoothing techniques in spatial statisticsWenceslao González-Manteiga (Spain)
Manuel Febrero-Bande (Spain)
3F2Smoothing sequences of data by extreme selectorsTertius de Wet (South Africa)
Willie Conradie
3F3Teaching non-parametric statistics to students in health sciencesMichael Joseph Campbell (United Kingdom)