What does it mean to say that we are going to use a sample to draw an inference about the population? Why is it random sample so important for this process? If we wanted a random sample of students in the cafeteria, why couldn't we just choose the students who ordered diet Pepsi with their lunch? Comment on the statement, "A random sample is like a miniature population, whereas samples that are not random are likely to be biased."
What does it mean to say that we are going to use a sample to draw an inference about the population? Why is it random sample so important for this process? If we wanted a random sample of students in the cafeteria, why couldn't we just choose the students who ordered diet Pepsi with their lunch? Comment on the statement, "A random sample is like a miniature population, whereas samples that are not random are likely to be biased."
A sample is considered as taking into account the whole population is impossible. For example, if I want to calculate the average height woman in a country. It is highly impossible to collect the data from all the women in the country. So I collect 10,000 woman at random and perform relevant statistics and conclude from the sample about the population data. For better inference, considering more than one sample is a common practice.
A statistical inference is done based on the laws of probability, sampling allows analysts to draw conclusions based on parameter statistics(sample mean, sample varianc, etc. ) performed on sample data regarding the population data. A random sample is crucial as the distrbution will be skewed if the sample drawn is not random.
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