What Is Considered A Large Sample Size

Statistics Definitions > Large Enough Sample Condition

Large Enough Sample Condition The Large Plenty Sample Status tests whether you have a big plenty sample size compared to the population. A general rule of thumb for the Large Plenty Sample Condition is that due north≥30, where northward is your sample size. However, it depends on what you lot are trying to accomplish and what you know about the distribution. In full general, the Large Enough Sample Status applies if any of these atmospheric condition are true:

You have a symmetric distribution or unimodal distribution without outliers: a sample size of xv is "big enough."
You have a moderately skewed distribution, that's unimodal without outliers; If your sample size is between sixteen and 40, it'due south "large enough."
Your sample size is >40, as long as yous practise non have outliers.
Your population has a normal distribution.

Central Limit Theorem and the Large Plenty Sample Rule

The central limit theorem states that if sample size are large enough, the distribution will be approximately normal. The full general rule of due north≥30 applies.

Chi Foursquare and the Big Enough Sample Status

There are iii different tests that employ the chi-square ; in each test, the assumptions and weather condition are the same, including the Large Enough Sample Condition. To know if your sample is large enough to use chi-square, yous must check the Expected Counts Condition: if the counts in every cell is 5 or more, the cells meet the Expected Counts Condition and your sample is large enough. Note that 5 is arbitrary and is open to interpretation. Some texts advise that it's okay to have a few expected counts less than 5 (no more than than 20%) as long as none are less than 1 (i.e. Yates, Moore & McCabe, The Do of Statistics, 1999).

Calculating sample size

Calculating sample size can be one of the most confusing aspects of statistics, generally because of all the rules (and rules of thumb) surrounding appropriate sizes for different distributions. You lot accept to brand certain your sample is sufficiently big, but non likewise large. For more techniques, run across: Sample Size (includes techniques like using tables and calculators).

Reference: Chi-Square John's Hopkins.

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