PSYC30013 Lecture Notes - Lecture 2: Test Statistic, Normal Distribution, Statistic
10 May 2018
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Lecture 2
- Under certain conditions the mean of sampling distribution equals to mean of population
value
- Standard error of sampling distribution provides a way of summarising what the average
variability in the sample statistic is likely to be
- Sampling distribution enables us to identify where the value of single sample statistic
might be located in relation to all possible values of that sample statistic, given an
assumed population parameter value; because the summary characteristics of the
sampling distribution can equal the parameter values of a corresponding mathematical
probability distribution
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- Standard deviation is a measure of average spread
- Mean is measure of average value
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Document Summary
Under certain conditions the mean of sampling distribution equals to mean of population value. Standard error of sampling distribution provides a way of summarising what the average variability in the sample statistic is likely to be. Standard deviation is a measure of average spread. Transformation into observed test statistics: converts a set of observed means into a set of values that are standardised and corresponds to an extended normal distribution. Lowercase z is for something from standard normal distribution z score is for individual person, z statistic is something that corresponds to standard normal distribution (and based on sample means) An observed score could be transformed into a standardised form, the most common of which was a z score. Same set of procedures can be equally applied to the value of a sample statistic. Especially when the values of a statistic form can be formed into a sampling distribution.
