Assertion: binomial's outcome | /r/HasProperty | success
Facets:
value: often
type: TEMPORAL
value: appropriately
type: MANNER
value: in rolling a die
type: MANNER
value: out of 1
type: OTHER
value: anywhere
type: LOCATION
value: in finding defective items
type: PURPOSE
value: in tossing a coin
type: TEMPORAL
value: most commonly
type: DEGREE
value: in other words
type: OTHER
value: traditionally
type: OTHER
Source sentences:
text: 3: Each observation represents one of two outcomes ("success" or "failure").
text: Suppose we conduct an experiment where the outcome is either "success" or "failure" and where the probability of success is p .
text: You know the probability of obtaining either outcome (traditionally called "success" and "failure") and want to know the chance of obtaining a certain number of successes in a certain number of trials.
text: The two outcomes are often labeled "success" and "failure" with success indicating the presence of the outcome of interest.
text: The binomial distribution model allows us to compute the probability of observing a specified number of "successes" when the process is repeated a specific number of times (e.g., in a set of patients) and the outcome for a given patient is either a success or a failure.
text: In general, the two possible outcomes are denoted as "success" and "failure" (what you consider as "success" is your chioce - it does not matter anyway; these are just two names).
text: As a result, whenever using the binomial distribution, we must clearly specify which outcome is the "success" and which is the "failure".
text: The outcome of interest in a trial of an experiment is often termed as a success.
text: The experiment consists of n repeated trials; Each trial results in an outcome that may be classified as a success or a failure (hence the name, binomial); The probability of a success, denoted by p, remains constant from trial to trial and repeated trials are independent.
text: With the parameters as defined above, the conditions for validity of the binomial distribution are each trial can result in one of two possible outcomes, which could be characterized as "success" or "failure".
text: These outcomes can be considered as either success or failure.
text: The outcome of each trial can be classified as either a success or a failure.
text: There are exactly two mutually exclusive outcomes of a trial: "success" and "failure".
text: In the theory of probability and statistics, a Bernoulli trial (or binomial trial) is a random experiment with exactly two possible outcomes, "success" and "failure", in which the probability of success is the same every time the experiment is conducted.
text: In other words, anywhere the outcome could be a success or a failure that can be proved through binomial distribution.
text: The outcome of each trial can either be a “success” or “failure”.
text: In a Bernoulli trial, the experiment is said to be random and could only have two possible outcomes: success or failure.
text: Each trial has two possible outcomes: success or failure.
text: Does each trial have only two possible outcomes — success or failure?
text: Each replication of the process results in one of two possible outcomes (success or failure), The probability of success is the same for each replication, and The replications are independent, meaning here that a success in one patient does not influence the probability of success in another.
text: In general, a binomial distribution applies when an experiment is repeated a fixed number of times, each trial of the experiment has two possible outcomes (success and failure), the probability of success is the same for each trial, and the trials are statistically independent.
text: A binomial distribution for a random variable X is one in which there are only two possible outcomes, success and failure, for a finite number of trials.
text: There can be only two outcomes – success
text: The probability of getting one outcome (success) p is held constant and the probability of getting the other outcome (failure) is also held constant, represented by (1 - p).
text: Only two possible outcomes, i.e. success or failure.
text: In a binomial distribution, there are only two possible outcomes, i.e. success or failure.
text: A Bernoulli trial is a probability experiment whose outcome is random such that there are two possible outcomes (success or failure).
text: Two possible outcomes for each trial or experiments are success and failure.
text: Most commonly the two outcomes of the experiment is said to be ‘Success’ or ‘Failure’.
text: For example, in tossing a coin, the outcome could be either success (HEADS) or failure (TAILS).
text: In finding defective items, the outcome could be either success (item is defective) or failure (item is non-defective).
text: In rolling a die, the outcome could be either success (one of the numbers out of 1-6 (say, six-6)) or failure (any of the numbers except) otherwise.
text: Consider an experiment having two possible outcomes: either success or failure.
text: The outcome of each trial can be either success (diamond) or failure (not diamond), and the probability of success is 1/4 in each of the trials.
text: Thus, each trial has two potential outcomes called "success" and "failure".
text: each trial has just two possible outcomes, called “ success ” (the outcome of interest) and “ failure
text: These outcomes are appropriately labeled "success" and "failure".
text: A binomial distribution with parameters (n,p) gives the discrete probability of having x successes out of n trials, with the probability of success p, assuming each trial is independent and the outcome of a trial is either a success or a failure.
text: Each trial has only two possible outcomes denoted as success or failure.