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generalisability
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The ability to apply the findings made on the basis of a representative sample to the population
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non-probability sampling
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In non-probability sampling not every unit has the same probability of being sampled
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normal distribution
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The Normal Distribution is a bell-shaped probability distribution which is symmetrical around the mean
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parameter
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A parameter is the value a statistic would assume in the long run. It is also called the Expected Value
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population
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Collection of all cases which possess certain pre-defined characteristics
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population distribution
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The probability distribution of the population
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probability
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Refers to how many times out of a total number of cases a particular event occurs. We can also see it as the chance of a particular event occurring
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probability sampling
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In probability sampling all units have the same probability of entering the sample. In addition, all possible combinations of n cases must have the same probability to be selected
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representative sample
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A sample which contains all characteristic of the population in accurate proportions
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sample
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A sub-group of the population
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sample distribution
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The probability distribution of a sample
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sampling
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The process of selecting sampling units from the population
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sampling distribution
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The probability distribution of a sample statistics, such as the mean. It can be derived from repeated sampling, or by estimation
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sampling error
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The extent to which the mean of the population and the mean of the sample differ from one another
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sampling method
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The way the sample is created
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standard error
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The standard deviation of the sampling distribution. It is defined as:\[\begin{equation*}\sigma_{\bar{y}} = \frac{\sigma}{\sqrt{n}}\end{equation*}\]
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z-score
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The z-score, sometimes also referred to as z-value, expresses in units of standard deviation how far an observation of interest falls away from the mean. It is defined as \[\begin{equation*}z = \frac{\text{observation} - \text{mean}}{\text{standard deviation}}=\frac{y-\mu}{\sigma}\end{equation*}\]
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