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Revision notes with simplified explanations to understand Sample Mean Distribution quickly and effectively.
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Take the random variable:
Consider the situation where we take a random sample of n items from this distribution and then find their mean. Repeat this a large number of times and list out all the means obtained. These will follow a normal distribution.
Example: Give the distribution of for when a sample of 60 items is taken, and the mean of the sample is found.
For the above, find and .
We can use this distribution of means in order to test whether the mean of a normal distribution has changed.
Example It is known that for a certain type of plant, its mean height is 21 cm. A new fertilizer is tested on the plant, and the manufacturer claims that it makes the plant grow taller. A sample of 20 such plants was chosen for testing with the fertilizer, and their mean height was found to be 21.2 cm.
Assuming that unfertilized plants' heights are normally distributed with mean 21 cm and standard deviation 1.5 cm, test at the 5% significance level whether the claim is true.
This is called the critical or rejection region. If our observation lies in this region, we reject . Method 1: Finding the critical region
Use your calculator in inverse normal mode to find the boundaries of the critical region.
Method 2: Finding probability to the left/right of observed value
Method 2:
Conclusion:
Example: The mean of a normally distributed population is known to be 5. A claim is made that this is not the mean. To test this, a sample of size 25 is taken, and the mean is found to be 6.1.
Given that the standard deviation of the population is 3, test at the 5% significance level whether the mean has changed from 5.
Probability Method:
This probability calculation is sometimes referred to as a p-value.
Accept : Insufficient evidence to suggest that the mean is different from 5 (at 5% significance level).
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