SOC222H5 Lecture Notes - Lecture 3: Standard Deviation, Frequency Distribution, Random Number Table
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Sampling: random sampling is the best sampling for statistics because everyone has the same chance of being selected, a sample is when you have a little portion of the population to get an accurate reading, ex. (called mu) , (called sigma: a statistic is used to describe the sample, these are usually denoted with regular letters (ex. If 3/10 people ride a bike to school in the sample, that is a statistic. However, if you refer it to the population, it becomes 60% of the population which is called the parameter: the main difference (sample) but almost never know the values of parameters (population) Probability sampling: probability sampling is the method that enables the researcher to specify for each case in the population the probability of being selected for the sample, only probability sampling allows us to use inferential statistics (random sample)
