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Your question is a bit difficult to understand. I will rephrase: In hypothesis testing, when the sample mean is close to the assumed mean of the population (null hypotheses), what does that tell you? Answer: For a given sample size n and an alpha value, the closer the calculated mean is to the assumed mean of the population, the higher chance that null hypothesis will not be rejected in favor of the alternative hypothesis.
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What happens to the sample mean as the sample size increases?
With a good sample, the sample mean gets closer to the population mean.
When using the distribution of sample mean to estimate the population mean what is the benefit of using larger sample sizes?
The variance decreases with a larger sample so that the sample mean is likely to be closer to the population mean.
What is population mean in statistics?
The population mean is the mean value of the entire population. Contrast this with sample mean, which is the mean value of a sample of the population.
Why is the sample mean an unbiased estimator of the population mean?
The sample mean is an unbiased estimator of the population mean because the average of all the possible sample means of size n is equal to the population mean.
What does it mean for a sample to representative of a population?
That the key characteristics of the population are reflected in the sample.
What happens to the sample mean as the sample size increases?
With a good sample, the sample mean gets closer to the population mean.
Is the sampling of error larger when the sample mean is closer to the population mean?
No.
When using the distribution of sample mean to estimate the population mean what is the benefit of using larger sample sizes?
The variance decreases with a larger sample so that the sample mean is likely to be closer to the population mean.
What is population mean in statistics?
The population mean is the mean value of the entire population. Contrast this with sample mean, which is the mean value of a sample of the population.
Why is the sample mean an unbiased estimator of the population mean?
The sample mean is an unbiased estimator of the population mean because the average of all the possible sample means of size n is equal to the population mean.
Difference between calculating the sample mean and the population mean?
The same basic formula is used to calculate the sample or population mean. The sample mean is x bar and the population mean is mu. Add all the values in the sample or population and divide by the number of data values.
What is the best estimator of population mean?
The best estimator of the population mean is the sample mean. It is unbiased and efficient, making it a reliable estimator when looking to estimate the population mean from a sample.
What is the difference between calculating the sample mean and the population mean?
You calculate the actual sample mean, and from that number, you then estimate the probable mean (or the range) of the population from which that sample was drawn.
The law of large numbers?
The law of large numbers states that as the number of observations in a sample increases, the sample mean will tend to approach the population mean. In other words, the larger the sample size, the more accurate the estimate of the population parameter. This law forms the basis for statistical inference and hypothesis testing.
What does it mean for a sample to representative of a population?
That the key characteristics of the population are reflected in the sample.
Is N the sample or population mean?
N is neither the sample or population mean. The letter N represents the population size while the small case letter n represents sample size. The symbol of sample mean is x̄ ,while the symbol for population mean is µ.
What is the the relationship between population and sample parameter and statistic?
The relations depend on what measures. The sample mean is an unbiased estimate for the population mean, with maximum likelihood. The sample maximum is a lower bound for the population maximum.
