Document Sample

Sampling and Probability Target Population – The entire group for which a study is performed. Matched Samples – Samples in which the members are paired or matched by the researcher or in samples that are taken multiple times on the same subjects under different circumstances. Independent Samples – Samples taken from a population that have no affect on one another. Random Sampling – A sampling method where a sample is chosen from a population on a random basis or by chance thus reducing the chance of bias in the sample. Each member of the population has a known but possibly non-equal chance of being selected. Simple Random Sampling – This is a Random Sampling method where the selection of members is made by chance where every member has an equal chance of being selected. Stratified Sampling – A sampling method where the members are selected equally from among the different strata or sub-groups of a population to ensure accurate representation of the population. Cluster Sampling – A sampling method whereby the population is divided into clusters and a random sample of the clusters is taken. All observations of a cluster are included in the sample. Quota Sampling – A sampling method where the members are pre-defined from a specified member type and thus is not a random sample and is not representative of the population. Spatial Sampling – An area of survey sampling concerned with sampling in two dimensions. Sampling Variability – The difference or variance in two or more samples taken from the same population. Standard Error – The deviation of the values of a given function of the data over all possible samples of the same size. Bias – A term referring to how far the average statistics lie from the actual parameter being estimated. Precision – A measure of how close an estimator is expected to be to the value of the parameter. Outcome – The result of an experiment involving uncertainty. Sample Space – An exhaustive list of all possible outcomes of an experiment usually denoted by S. Event – Any collection of outcomes of an experiment. Relative Frequency – Another term for proportion and is calculated by dividing the number of times an event occurs by the total number of times an experiment is carried out. Probability – Provides a quantitative description of the likely occurrence of a particular event. Subjective Probability – A reasonable assessment by an individual concerning the likelihood a particular event will occur. Independent Events – Events that have no correlation or affect on one another. Mutually Exclusive Events – Events that can not occur together. Addition Rule – A rule used to determine the probability that event A or event B occurs or both occur. Multiplication Rule – A rule used to determine the probability that event A and event B both occur. Conditional Probability – A probability calculated given the current conditions. With a change in conditions the probability may change. Law of Total Probability – A calculation to express the state that a probability of and event C occurring will be affected by the probabilities of events A and B of which C is a subset. Bayes’ Theorem – A result that allows new information to be used to update the conditional probability of an event. Random Variable – A function that associates a unique numerical value with every outcome of an experiment. The value of the variable will vary as the experiment is repeated. Expected Value – An expected value of a random variable indicates its average or central value. Variance – A non-negative number which gives an idea of how widely spread the values of a random variable are likely to be. Probability Distribution – A list of probabilities associated with each of the values of a discrete random variable. Cumulative Distribution Function – A function giving the probability that the random variable X is less than or equal to x, for every value x. Probability Density Function – A function which can be integrated to obtain the probability that the random variable takes a value in a given interval. Discrete Random Variable – A random variable that can only take on a countable number of distinct values. Continuous Random Variable – A random variable which takes an infinite number of possible values. Probability-Probability (P-P) Plot – Used to see if a given set of data follows some specified distribution. Quantile-Quantile (QQ) Plot – Used to see if a given set of data follows some specified distribution. Normal Distribution – A method of modeling continuous random variables. Poisson Distribution – A method of modeling discrete random variables. Binomial Distribution – A method of modeling discrete random variables. Geometric Distribution – A method of modeling discrete random variables. Uniform Distribution – A method of modeling continuous random variables and discrete random variables. Independent Random Variables – These are random variables that do not have any influence on one another. Central Limit Theorem – States that data that are affected by many small and unrelated random affects are normally distributed.

DOCUMENT INFO

Shared By:

Categories:

Tags:
Probability Sampling, sample size, simple random sampling, sampling frame, random sampling, target population, sampling method, stratified sampling, Sampling error, Cluster sampling

Stats:

views: | 141 |

posted: | 1/13/2010 |

language: | English |

pages: | 2 |

How are you planning on using Docstoc?
BUSINESS
PERSONAL

By registering with docstoc.com you agree to our
privacy policy and
terms of service, and to receive content and offer notifications.

Docstoc is the premier online destination to start and grow small businesses. It hosts the best quality and widest selection of professional documents (over 20 million) and resources including expert videos, articles and productivity tools to make every small business better.

Search or Browse for any specific document or resource you need for your business. Or explore our curated resources for Starting a Business, Growing a Business or for Professional Development.

Feel free to Contact Us with any questions you might have.