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Will margins of error for sample of size 80 be larger or smaller than those for sample size of 40?
Wiki User
∙ 12y ago
They should be smaller for the sample size 80.
Wiki User
∙ 12y ago
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How can reduce the percentage error?
The larger the sample, the lower the % error.. so to reduce a % error, increase your sample size.
How To get valid results small samples are sufficient?
I will assume the sample is random. In general, the larger the sample, the smaller the percentage error will be (the difference between percentages in the sample, and the percentages in the universe from whence the sample is taken). The percentage error tends to go down as the square root of the size of the sample.
Can increasing sample size reduce random error?
Random error and sample size have an inverse relationship...As sample size INCREASES random error DECREASES. There's a good explanation at the related link.
Is the standard error of the sample mean assesses the uncertainty or error of estimation?
yes
How to reduce Margin of error?
Increase sample size.
How does sample size affect the margin of error?
The larger the sample size, the smaller the margin of error.
How can reduce the percentage error?
The larger the sample, the lower the % error.. so to reduce a % error, increase your sample size.
How does sample size affect the size of your standard error?
The standard error should decrease as the sample size increases. For larger samples, the standard error is inversely proportional to the square root of the sample size.The standard error should decrease as the sample size increases. For larger samples, the standard error is inversely proportional to the square root of the sample size.The standard error should decrease as the sample size increases. For larger samples, the standard error is inversely proportional to the square root of the sample size.The standard error should decrease as the sample size increases. For larger samples, the standard error is inversely proportional to the square root of the sample size.
How To get valid results small samples are sufficient?
I will assume the sample is random. In general, the larger the sample, the smaller the percentage error will be (the difference between percentages in the sample, and the percentages in the universe from whence the sample is taken). The percentage error tends to go down as the square root of the size of the sample.
Describe how the sample size affects the standard error?
Standard error (which is the standard deviation of the distribution of sample means), defined as σ/√n, n being the sample size, decreases as the sample size n increases. And vice-versa, as the sample size gets smaller, standard error goes up. The law of large numbers applies here, the larger the sample is, the better it will reflect that particular population.
Is the sampling of error larger when the sample mean is closer to the population mean?
No.
Why would having a larger sample size be a better idea than having a samll sample size when doing an experiment?
less bias and error occur when sample size is larger
Why should the sample size of the control group be the same as the size of the experimental group?
In a scientific experiment, the control group and the experimental group are treated the same way except for the variable being tested. Because the margins of error increase as the sample size gets smaller, both groups should be the same size.
What does it mean when the standard error value is smaller than the standard deviation?
It simply means that you have a sample with a smaller variation than the population itself. In the case of random sample, it is possible.
Why is the standard error a smaller numerical value compared to the standard deviation?
Let sigma = standard deviation. Standard error (of the sample mean) = sigma / square root of (n), where n is the sample size. Since you are dividing the standard deviation by a positive number greater than 1, the standard error is always smaller than the standard deviation.
How does increasing the sample size affect the sample error of the mean?
It should reduce the sample error.
Relationship between type 1 error and type 2 error?
In statistics, there are two types of errors for hypothesis tests: Type 1 error and Type 2 error. Type 1 error is when the null hypothesis is rejected, but actually true. It is often called alpha. An example of Type 1 error would be a "false positive" for a disease. Type 2 error is when the null hypothesis is not rejected, but actually false. It is often called beta. An example of Type 2 error would be a "false negative" for a disease. Type 1 error and Type 2 error have an inverse relationship. The larger the Type 1 error is, the smaller the Type 2 error is. The smaller the Type 2 error is, the larger the Type 2 error is. Type 1 error and Type 2 error both can be reduced if the sample size is increased.
