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The sample should not be normally distributed.
If you have a population of size N from which a random sample of size n is to be drawn, then there are NCn possible samples. Each one of these must have the same probability of being thesample. That is, the sample is uniformly distributed - not Normally.
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When the population is normally distributed population standard deviation s is unknown and the sample size is n equals 15 the confidence interval for the population mean s is based on the?
The Z test.
What are the best statistics to use for data that is normally distributed?
The mean and standard deviation. If the data really are normally distributed, all other statistics are redundant.
When do you use the central limit theorem?
You use the central limit theorem when you are performing statistical calculations and are assuming the data is normally distributed. In many cases, this assumption can be made provided the sample size is large enough.
Is it true that the mean of a normally distributed variable can be any real number?
no
Is a type of data that is likely to be normally distributed?
The means of repeated samples from any population.
When you draw a sample from a normal distribution what can you conclude about the sample distribution?
The answer depends on how the sample is selected. If it is a simple random sample, of size n, then it is distributed approximately normally with the same mean as the population mean.The answer depends on how the sample is selected. If it is a simple random sample, of size n, then it is distributed approximately normally with the same mean as the population mean.The answer depends on how the sample is selected. If it is a simple random sample, of size n, then it is distributed approximately normally with the same mean as the population mean.The answer depends on how the sample is selected. If it is a simple random sample, of size n, then it is distributed approximately normally with the same mean as the population mean.
When is the sample mean over repeated samples from the same population or process not normally distributed?
Provided the samples are independent, the Central Limit Theorem will ensure that the sample means will be distributed approximately normally with mean equal to the population mean.
Explain why a selection of 10 students from your class can have marks that aren't normally distributed when the marks of the whole class are normally distributed?
The sample size is likely to be too small.
Why do population follow normal distribution?
The form of this question incorportates a false premise. The premise is that the data are normally distributed. Actually, is the sample mean which, under certain circumstances, is normally distributed.
Why does normal distribution occur when samples get larger?
That only happens when you sample a population that is normally distributed. In that case, the question and its answer are quite circular.
Assumption of one-way analysis of variance?
The results of a one-way ANOVA can be considered reliable as long as the following as The results of a one-way ANOVA can be considered reliable as long as the following assumptions are met: * Response variable must be normally distributed (or approximately normally distributed). * Samples are independent. * Variances of populations are equal. * The sample is a Simple Random Sample (SRS). ANOVA is a relatively robust procedure with respect to violations of the normality assumption (Kirk, 1995) If data are ordinal, a non-parametric alternative to this test should be used - Kruskal-Wallis one-way analysis of variance. sumptions are met: * Response variable must be normally distributed (or approximately normally distributed). * Samples are independent. * Variances of populations are equal. * The sample is a Simple Random Sample (SRS). ANOVA is a relatively robust procedure with respect to violations of the normality assumption (Kirk, 1995) If data are ordinal, a non-parametric alternative to this test should be used - Kruskal-Wallis one-way analysis of variance
What are the assumptions underlying the two sample t test?
The assumptions of a two-sample t-test are: Each sample come from a normally distributed population. Both populations have equal variances. The data are sampled independently from each population.
Is a normally distributed variable needed to have a normally distributed sampling distribution.?
Yes, it is.
Can you think of a variable that is normally distributed?
The value of a roll of two dice is normally distributed.
Does the central limit theorem allows the use of the normal distribution to analyze the sample mean if the sample sizes are large enough?
Yes. Roughly, very large samples are very likely to have subsets data points having very similar means and distributions. Large numbers of such subsets will tend to be normal distributed (Why?) and will tend to make the total sample be normally distributed.
True or false a distribution of sample means is normally distributed with a mean equal to the population mean and standard deviation equal to the standard error of the mean?
True.
What should a well-constructed histogram reveal to you?
Whether or not the data are normally distributed and the Customer expectations.
