Psy 202, Chapter 5
Psy 202, Chapter 5 Psy 202
Popular in Elementary Statistics
Popular in Psychology (PSYC)
This 2 page Class Notes was uploaded by T'Keyah Jones on Tuesday September 13, 2016. The Class Notes belongs to Psy 202 at University of Mississippi taught by Dr. Melinda Redding in Fall 2016. Since its upload, it has received 4 views. For similar materials see Elementary Statistics in Psychology (PSYC) at University of Mississippi.
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Date Created: 09/13/16
September 13, 2016 a. Types of Samples Representative sample The proportions of the variable in the population are accurately reflected in the sample Convenience sample Common, but unlikely to be representative Sample is convenient Random sample Is one in which all cases in the population have an equal chance of being selected Does not guarantee representative sample All combinations are possible with truly random sample b. Problems with Sampling Self-selection Bias Occurs when not everyone who is asked to participate in a study agrees to do so Sampling Error Reflects discrepancies between sample values and population values due to random factors Not systematic There is a difference between sample statistic and population parameter c. Sampling Distribution Distribution made up of sample statistics Generated by: - Taking repeated random samples of a specific size from a population - Calculating some statistics (like a mean) for each sample - Making a frequency distribution of those values The standard deviation of this distribution is called the standard error of the mean ( )M M M Grand mean of means True population mean d. Central Limit Theorem Tells us three things: - If N is large The sample distribution of the mean will be normally distributed No matter what they shape of the population distribution or the shape of the population distribution - The mean of the sampling distribution Is the same as the mean of the population from which the samples were selected - The Standard Error of the M Standard deviation of the sampling distribution can be calculate with this formula: M= √N When is unknown, the standard deviation of sample can be used to estimate the standard error of the mean: sM= s √N
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