Central Limit Theorem and Standard Error
The central limit theorem says that sample means follow a roughly normal distribution as the sample grows, whatever the shape of the original population. The standard error measures how much those sample means spread, and it shrinks as the sample size increases.
Central Limit Theorem and Standard Error: what to notice and how to work it
What to notice first
Common student mistake
Key formulas and cues
A reliable path
- Identify the questionDecide whether you need center, spread, shape, or association.
- Use the right displayChoose a histogram, box plot, scatter plot, or summary statistic.
- Write the meaningExplain what the statistic says about the data set.
Worked examples
Find IQR
- IQR measures the middle 50%.
- Subtract Q1 from Q3.
- 20 – 8 = 12.
Read association
- As x increases, y tends to increase.
- That is a positive association.
- A best-fit line should have positive slope.
Try one before moving on
Central Limit Theorem and Standard Error: pop-up practice
Central Limit Theorem and Standard Error , Example 1:
Central Limit Theorem and Standard Error , Example 2:
Solution: First, find the mean of the given data.
Mean(=frac{4+8+12+16+20}{5}=12)
Now, the standard deviation can be calculated as;
(S=frac{Summation:of:difference:between:each:value:of:given:data:and:the:mean:value}{Number:of:values})
(S=sqrt{frac{left(4-12right)^2+left(8-12right)^2+left(12-12right)^2+left(16-12right)^2+left(20-12right)^2}{5}})
(=5.65)
So, use the (SE) formula: (SE=frac{σ}{sqrt{n}})
(SE=frac{5.65}{sqrt{5}}= 2.52)
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