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Introduction Statistics are used everyday in life and are very important in the everyday world One important use of statistics is to summarize a collection of data in a clear and understandable way For example if a psychologist gave a personality test measuring shyness to all 2500 students attending a small college How might these measurements be summarized There are two basic methods numerical and graphical Using the numerical approach one might compute statistics such as mean and standard deviation These statistics convey information about the average degree of shyness and the degree to which people differ in shyness Using the graphical approach you could create a stem and leaf display and a box plot These plots contain detailed information about the distribution of shyness scores Graphical methods are better than numerical methods for identifying patterns in the data Numerical approaches are more accurate and objective Since the numerical and graphical approaches compliment each other you should use both Inferential statistics Inferential statistics are used to draw inferences about a population from a sample An example is if ten people who performed a task after twenty-four hours without sleep scored 12 points lower than ten people who performed after a normal nights sleep Is the difference real or could it be due to chance How much larger could the real difference be than the 12 points These are the types of questions answered by inferential statistics There are two main methods used in inferential statistics estimation and hypothesis testing In estimation the sample is used to estimate a parameter and a confidence interval about the estimate is constructed In the most common use of hypothesis testing a straw man null hypothesis is put forward and it is determined whether the data are strong enough to reject it For the sleep deprivation study the null hypothesis would be that sleep deprivation has no effect on performance
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