Assertion: distribution | /r/HasProperty | normal
Facets:
value: with a mean
type: OTHER
value: nearly
type: DEGREE
value: against the alternative
type: OTHER
value: with expected value
type: OTHER
value: asymptotically
type: DEGREE
Source sentences:
text: This result is sometimes loosely stated by saying that the distribution of X is asymptotically normal with expected value np and variance np(1 − p).
text: This distribution is normal N ( μ , σ 2 / n ) {
text: This distribution is called 'normal' in the sense of 'ideal' or 'standard' against which other distributions may be compared.
text: —for those processes, even if the distribution isn’t considered "normal.
text: Therefore, physical quantities that are expected to be the sum of many independent processes (such as measurement errors) often have distributions that are nearly normal.
text: However, supposing we manage to reject the zero mean hypothesis, even if we know the distribution is normal and variance is 1, the null hypothesis test does not tell us which non-zero value we should adopt as the new mean.
text: For example, when testing the null hypothesis that a distribution is normal with a mean less than or equal to zero against the alternative that the mean is greater than zero (variance known), the null hypothesis does not specify the probability distribution of the appropriate test statistic.
text: Distributions that are normal or Gaussian have the following characteristics:
text: Because the distribution of this data is normal, you can determine exactly what percentage of the values fall in any specific range.