Assertion: sample's distribution | /r/HasProperty | continuous
Facets:
value: both
type: DEGREE
value: absolutely
type: DEGREE
value: occasionally
type: DEGREE
Source sentences:
text: If the distribution of X is continuous, then X is called a continuous random variable.
text: This distribution was discrete, since there were a finite number of possible observations.
text: For this simple example, the distribution of pool balls and the sampling distribution are both discrete distributions.
text: The standard error is The sampling distribution of p is a discrete rather than a continuous distribution.
text: The variable "color of M&M" used in this example is a discrete variable, and its distribution is also called discrete.
text: Occasionally, the categorical distribution is termed the "discrete distribution".
text: Distributions for continuous variables are called continuous distributions.
text: This definition includes the (absolutely) continuous distributions defined above, but it also includes singular distributions, which are neither absolutely continuous nor discrete nor a mixture of those, and do not have a density.
text: There exist distributions that are neither continuous nor discrete.
text: To use the normal approximation to calculate this probability, we should first acknowledge that the normal distribution is continuous and apply the continuity correction.
text: Of course, the discrete distributions are discrete and the continuous distributions are continuous, so there's some difference just from that aspect alone, but as far as the computer is concerned, they're all the same.
text: In statistics, when a p-value is used as a test statistic for a simple null hypothesis, and the distribution of the test statistic is continuous, then the p-value is uniformly distributed between 0 and 1 if the null hypothesis is true.
text: If the distribution is discrete, the individual probabilities of all possible values of x j {\displaystyle x_{j}} If the distribution is continuous and of a known form, the normalization constant will also be known.
text: It is customary to apply a continuity correction factor \(cf = \frac{0.5}{n}\) to compensate for the fact that the underlying distribution is discrete, especially when the sample size is not sufficiently large.
text: When a sample (a set of observations) is drawn from a larger population, the sample points have an empirical distribution that is discrete and that provides information about the population distribution.
text: Of course, the discrete distributions are discrete and the continuous distributions are continuous, so there's some difference just from that aspect alone, but as far as the computer is concerned, they're all the same.
text: If the distribution is discrete, the individual probabilities of all possible values of x j {\displaystyle x_{j}} If the distribution is continuous and of a known form, the normalization constant will also be known.
text: For this simple example, the distribution of pool balls and the sampling distribution are both discrete distributions.
text: For this simple example, the distribution of pool balls and the sampling distribution are both discrete distributions.
text: For this simple example, the distribution of pool balls and the sampling distribution are both discrete distributions.