Assertion: sample | /r/HasProperty | large
Facets:
value: sufficiently
type: DEGREE
value: relatively
type: DEGREE
value: in each group
type: LOCATION
Source sentences:
text: When the sample is large, the sampling distribution of a proportion will have an approximate normal distribution.
text: The central limit theorem states that if the sample is large enough, its distribution will approximate that of the population you took the sample from.
text: The sample is said to be large when n ≥ 25.
text: Yet the probability of having a very poor sample tends to be quite small, as long as the samples are random and sufficiently large.
text: For samples that are sufficiently large it turns out that the mean of the sample is normally distributed (the Central Limit Theorem), and so the techniques described for the normal distribution can be used.
text: If you are comparing means and need to decide between the Z and t distributions, it does not matter if sigma (greek) is known or not if the sample is large enough in each group, greater than 30–you can use either Z or t because they become similar enough past n = 30.
text: -->for example, don't mistakenly think that the CLT says that data are normally distributed as long as the sample is large enough
text: The ratio of 1/N to 1/(N − 1) approaches 1 for large N, so the maximum likelihood estimate approximately equals the unbiased estimate when the sample is large.
text: Determining sample size is a very important issue because samples that are too large may waste time, resources and money, while samples that are too small may lead to inaccurate results.
text: , if the sample is relatively large.