1) A sampling plan is a detailed outline of which measurements will be taken at what times, on which material, in what manner, and by whom. Sampling plans should be designed in such a way that the resulting data will contain a representative sample of the parameters of interest and allow for all questions, as stated in the goals, to be answered.

Attributes of a sample:

• Every individual in the chosen population should have an equal chance to be included in the sample.
• Ideally, choice of one participant should not affect the chance of another's selection (hence we try to select the sample randomly - thus, it is important to note that random sampling does not describe the sample or its size as much as it describes how the sample is chosen).

Effect size from the original study and its MoE:

Statistical significance specifies, if a result may not be the cause of random variations within the data. But not every significant result refers to an effect with a high impact, resp. it may even describe a phenomenon that is not really perceivable in everyday life. Statistical significance mainly depends on the sample size, the quality of the data and the power of the statistical procedures. If large data sets are at hand, as it is often the case f. e. in epidemiological studies or in large scale assessments, very small effects may reach statistical significance. In order to describe, if effects have a relevant magnitude, effect sizes are used to describe the strength of a phenomenon.

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