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- Confidence Intervals: Why n gt;30 is Acceptable as Population Representative?
Well, here is the answer: in general, can we accept that when sample size is greater than 30 items (n > 30), then sample standard deviation s, tends to the standard deviation of the population, σ
- why n gt;=30 for central limit theorem to hold? [duplicate]
From population choosing samples (size n=30) and calculate its mean then repeating it N times will converge to normal distribution as N->inf when mean of each sample is plotted as a histogram
- Is n = 30 really enough? A popular inductive fallacy among data . . .
Mathematically, if X₁, X₂, X₃,… are random samples, each of size n, drawn from the population P with population mean μ and standard deviation σ, then the transformation Z = (X̄-μ) (σ √n) follows a Normal distribution with mean 0 and variance 1 as the size of n increases
- Why is n gt;= 30 considered a large sample in statistics? - Reddit
CLT tells us that if we know the std deviation sigma, that sqrt (n) (sigma) * ( (sample mean) - mu) converges in distribution to N (0,1) as n to infinity There are a couple important differences between what this says and what you said
- The Large Sample Condition: Definition Example - Statology
In order to answer this question, we can use the Normal CDF Calculator, but we first need to verify that the sample size is large enough in order to assume that the distribution of the sampling mean is normal In this example, our sample size is n = 100, which is much larger than 30
- Why we use t-distribution when sample size is small? $(n lt; 30)$
When population variance σ2 σ 2 is unknown and estimated by sample variance S2, S 2, you should always use a t test for most accurate results For n> 30 n> 30 results from an incorrect z test may be a serviceable approximation
- Large Enough Sample Condition - Statistics How To
The Large Enough Sample Condition tests whether you have a large enough sample size compared to the population A general rule of thumb for the Large Enough Sample Condition is that n ≥ 30, where n is your sample size
- t-test for more than 30 samples? - Cross Validated
The t-test works for any sample size There's nothing magical about n=30; indeed in my book the t-test isn't much like the z near typical significance levels (5% to 1%) until well past n=30 Eventually the tables become very close to the z-tables a fair way into the tail
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