You calculate the actual sample mean, and from that number, you then estimate the probable mean (or the range) of the population from which that sample was drawn.
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a "T" or a "Z" score. A "T" Score if comparing a sample. A "Z" Score when comparing a population. Remember, a population includes all observation, and a sample includes only a random selection of the population.
A sample is a subset of the population.
Non-probability or Judgement Samples has to do with a basic researcher assumptions about the nature of the population, the researcher assumes that any sample would be representative to the population,the results of this type of samples can not be generalized to the population(cause it may not be representative as the research assumed) and the results may be biased. Probability or Random samples is a sample that to be drawn from the population such that each element in the population has a chance to be in the selected sample the results of the random samples can be used in Statistical inference purposes
In Statistics, the measure of spread tells us how much adata sample is spread out or scattered. We can use the range and the interquartile range (IQR) to measure the spread of a sample. Measures of spread together with measures of location (or central tendency) are important for identifying key features of a sample to better understand the population from which the sample comes from. The range is the difference between a high number and the low number in the samples presented. It represents how spread out or scattered a set of data. It is also known as measures of dispersion or measures of spread.
A random sample is a sample (subset of the population) where each member of the population has an equal chance of being sampled. See related links.