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How to get the sample size of a large and a unkown population in terms of statistics?
Kisuba Muliro Daniel
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The law of large numbers?
The law of large numbers is a principle of probability and statistics. It states that as a sample size increases, its mean will get closer to the average of the whole population.
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 are the advantages and disadvantages of inferential statistics?
One advantage of inferential statistics is that large predictions can be made from small data sets. However, if the sample is not representative of the population then the predictions will be incorrect.
What is the definition of Sample Size?
This question gets at the core of statistics. Statistics has two central ideas: a population, and a sample. All of statistics is an attempt of ways to understand the entire population without actually observing every entity within it - by choosing and observing only a sample (a sub-set) of the full population. As an example, if we wish to find the average ratio of males to females on the entire planet, we can do this one of two ways. Either we actually count every person on the planet and after a very lengthy process we will have the answer. Or alternatively, we can use statistics to simplify the effort by selecting a smaller group of people (we call this a sample) that we believe will accurately represent, without any bias, the entire planet's population. If we take this second approach, also called a sampling approach, one of the most important decision to take is the number of people to include in this group (in the sample). This number - known as the "Sample Size" - is the number of a population that will be evaluated as representing the entire population, and from which statistics will be derived. Choosing a sample size too large will require extra unnecessary effort, but one too small will not accurately represent the entire population (and the statistics or averages derived might not be correct - we won't even know IF the statistics are correct or incorrect - so we won't know if we need to repeat with another sample).
A sample average can be used to estimate a population average precision if the sample is?
large
Explain the differences between a sample and a population?
A sample consists of a small portion of data when a population is taken from a large amount.
What two features must a sample have if its to accurately represent a population?
The sample must be large and random.
Define population and sample?
The population is the set of all things which you wish to study. However, because collecting information from a large, possibly infinite, population is likely to be prohibitively large and time consuming, it is collected from only some members of the population. This subset is a sample.The population may, but need not, consist of people. It could be the set of cars, or plots of land. There are a number of different ways of selecting samples: how the sample is selected will influence the quality of the statistics collected and, therefore, the validity of any conclusions.
What does it means if the standard deviation is large?
that you have a large variance in the population and/or your sample size is too small
Differentiate estimate and estimator?
It can get a bit confusing! The estimate is the value obtained from a sample. The estimator, as used in statistics, is the method used. There's one more, the estimand, which is the population parameter. If we have an unbiased estimator, then after sampling many times, or with a large sample, we should have an estimate which is close to the estimand. I will give you an example. I have a sample of 5 numbers and I take the average. The estimator is taking the average of the sample. It is the estimator of the mean of the population. The average = 4 (for example), this is my estmate.
In statistics what is the fundamental assumption?
You may be referring to the Central Limit Theorem.The Central Limit Theorem states that if you draw a large enough random sample from any population with a finite variance, the distribution of that sample will be approximately Normal (i.e. it will follow a Gaussian, or classic "Bell Shaped" pattern).
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...
