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In probability theory, the sample space (also called sample description space or possibility space) of an experiment or random trial is the set of all possible outcomes or results of that experiment. A sample space is usually denoted using set notation, and the possible ordered outcomes, or sample points, are listed as elements in the set. It is common to refer to a sample space by the labels S, Ω, or U (for "universal set"). The elements of a sample space may be numbers, words, letters, or symbols. They can also be finite, countably infinite, or uncountably infinite.

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rdfs:label	Sample space (en)
rdfs:comment	In probability theory, the sample space (also called sample description space or possibility space) of an experiment or random trial is the set of all possible outcomes or results of that experiment. A sample space is usually denoted using set notation, and the possible ordered outcomes, or sample points, are listed as elements in the set. It is common to refer to a sample space by the labels S, Ω, or U (for "universal set"). The elements of a sample space may be numbers, words, letters, or symbols. They can also be finite, countably infinite, or uncountably infinite. (en)
sameAs	Sample space
dbp:wikiPageUsesTemplate	dbt:Commons_category-inline dbt:Main dbt:Reflist dbt:Rp dbt:Closed-open dbt:Probability_fundamentals
Subject	Experiment (probability theory)
gold:hypernym	Set
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Sample_space?oldid=1061664811&ns=0
Wikipage page ID	22958 (xsd:integer)
page length (characters) of wiki page	14489 (xsd:nonNegativeInteger)
Wikipage revision ID	1061664811 (xsd:integer)
has abstract	In probability theory, the sample space (also called sample description space or possibility space) of an experiment or random trial is the set of all possible outcomes or results of that experiment. A sample space is usually denoted using set notation, and the possible ordered outcomes, or sample points, are listed as elements in the set. It is common to refer to a sample space by the labels S, Ω, or U (for "universal set"). The elements of a sample space may be numbers, words, letters, or symbols. They can also be finite, countably infinite, or uncountably infinite. A subset of the sample space is an event, denoted by . If the outcome of an experiment is included in , then event has occurred. For example, if the experiment is tossing a single coin, the sample space is the set , where the outcome means that the coin was heads and the outcome means that the coin was tails. The possible events are and . For tossing two coins, the sample space is , where the outcome is if both coins are heads, if the first coin is heads and the second is tails, if the first coin is tails and the second is heads, and if both coins are tails. For tossing a single six-sided die, where the result of interest is the number of pips facing up, the sample space is . A well-defined, non-empty sample space is one of three components in a probabilistic model (a probability space). The other two basic elements are: a well-defined set of possible events (an event space), which is typically the power set of if is discrete or a σ-algebra on if it is continuous, and a probability assigned to each event (a probability measure function). A sample space can be represented visually by a rectangle, with the outcomes of the sample space denoted by points within the rectangle. The events may be represented by ovals, where the points enclosed within the oval make up the event. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Sample_space
is Wikipage redirect of	Probability/Sample space Sample description space Sample spaces SampleSpace Sample Space Universal sample space
is Link from a Wikipage to another Wikipage of	List of statistics articles Almost surely Assured clear distance ahead Bayes' theorem Bayesian statistics Bernoulli scheme Buffon's noodle CURE algorithm Catalog of articles in probability theory Categorical distribution Glossary of engineering: M–Z Glossary of probability and statistics Jan Visman Dirac measure Discrete mathematics Doob's martingale convergence theorems Jensen's inequality Odds List of probability topics Collectively exhaustive events Complementary event Compositional data Conditional probability Convergence of random variables Elementary event Gambling mathematics Independence (probability theory) Independent and identically distributed random variables Experiment (probability theory) Estimator Event (probability theory) Loss functions for classification Law of total expectation Law of total probability Mathematical statistics Maximum likelihood estimation Multivariate random variable
is known for of	Richard von Mises

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