Topic 6_Introduction to Inferential Statistics & Estimation Procedures.pdf

# Topic 6_Introduction to Inferential Statistics & Estimation Procedures.pdf

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Introduction to Inferential Statistics and Estimation Procedures Chapters 6 and 7

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By now we know (or should know!).… Most variables approximate well to a normal distribution. The unique characteristics of the normal distribution are is symmetrical on any normal curve, the distance from any given point to the mean (when measured in standard deviations) will cut- off exactly the same proportion of the total area
By now we know (or should know!) (contd.).… Further, a normal curve can be thought of as a distribution of probabilities – that is the probability that a randomly selected case from an empirical normal distribution will have a score that falls in a certain range can be estimated. The above make the assumption of normal distribution central to the inferential statistics – generalizing from a sample to a population.

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In This Topic…. Sampling procedures and sampling error Population, sample, and sampling distribution z and t – statistics and standard error Constructing and interpreting confidence intervals
Statistics are based on samples, not populations. sample: statistics ; population: parameters Population: The entire set of persons, objects, or events that has a least one common characteristic of interest to the researcher. represented by Greek letters Sample: A subset of cases or elements selected from a population. represented by English letters Quick Review

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Sampling Procedures Through the sampling process, social researchers seek to generalize from a sample (a small group) to the entire population (a larger group). There are a multitude of sampling procedures that social researches use to obtain samples (many of which you will learn or have learnt in the course on research methods). The goal of any sampling procedure is to maximize chances of representativeness of the selected sample to the population and this chance is to follow the principle of EPSM (Equal Probability of Selection Method). The most the prominent sampling technique using EPSM is that of Random Sample Selection, or Random Sampling .
Random Sampling Defined as: A procedure for selecting a set of representative observations from a population, in which each observation has a equal chance of being selected for the sample. (Remember the probability discussion). Can be done with or without replacement. Simple random sampling, systematic sampling, stratified sampling, and cluster sampling are the various techniques of random sampling. Inferential statistics is based on the assumption that samples are randomly selected.

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Sampling Error If the sample accurately reflects the population, why do we have two sets of notation? This is because there will almost always be some difference between the population and the sample.
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• Fall '11
• Gemelli

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