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A histogram representing a sample taken from a population that is uniform?
For a large enough sample, it will resemble a rectangle whose base will be the range of the variable and the height will be the reciprocal of the number (or width) of the base.
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.
Why use sample instead of population?
When performing an experiment or gathering data for statistics, it would be very difficult to gather information for every member of the group's population. Instead, one can gather information from a sample large enough to be representative of the population.
Why does a statistical sample have to be large?
The larger the sample of data collected leads to a more accurate conclusion.
Characteristics of a good sample?
1. the sample should be representative thus carefully selected. 2. the sample should be adequate thus significant enough.
Is Darmstadtium a solid?
No large enough sample has been prepared to know what the phase is.
How large should the sample be to be large enough?
Span the full spectrum of a population's genetic variation. <apex> Reflects the genetic variation of a population...
A histogram representing a sample taken from a population that is uniform?
For a large enough sample, it will resemble a rectangle whose base will be the range of the variable and the height will be the reciprocal of the number (or width) of the base.
Why is a large sample better than a small sample?
A large sample will reduce the effects of random variations.
Does anti-matter weigh anything?
It seems reasonable to assume it does, but it has not been possible to get a large enough sample (or have measurements accurate enough) to confirm how it reacts to gravity.
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.
How can you compare means of two samples when the samples are chi square distributed?
According to the Central Limit Theorem if the sample size is large enough then the means will tend towards a normal distribution regardless of the distribution of the actual sample.
Disadvantages of a large sample size confidence Interval in statistices?
A disadvantage to a large sample size can skew the numbers. It is better to have sample sizes that are appropriate based on the data.
A sample average can be used to estimate a population average precision if the sample is?
large
Why use sample instead of population?
When performing an experiment or gathering data for statistics, it would be very difficult to gather information for every member of the group's population. Instead, one can gather information from a sample large enough to be representative of the population.
What is used to create a large sample of DNA for testing for a small sample of DNA?
PCR
What two features must a sample have if its to accurately represent a population?
The sample must be large and random.
