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Q: If a sample of 187 bowlers were taken from a population of 999 bowlers could refer to the mean of how many bowlers' scores?

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Sample is subset of the population so sample size and population size is different.However, as a subset can be the whole set, if the sample size equals the population size, you have sampled the entire population and you will be 100% accurate with your results; it may cost much more than surveying a [representative] sample, but you get the satisfaction of knowing for what you surveyed the population exactly.Using a sample is a trade off between the cost of surveying the whole population and accuracy of the result.A census is a survey of the whole population and could be considered that the sample size = population size; in this case the results are 100% accurate.The television viewing figures are calculated using a sample of the whole population and then extrapolating them to the whole population; depending upon how the same was chosen, including its size, will affect the accuracy of the results - most likely not more than 95% accurate.With a carefully selected (that is properly biased) sample you can prove almost anything!

We could answer that in a snap, if we knew something about the population and could see the list of proposed samples. Call me crazy, but I'm suspecting wherever you copied the question from, all that stuff was right there with it.

its better because we often don't have to survey a large population, so a sample is quicker, easier, requires few ressources, little time and can be more accurate if a person is not there to answer it because a sample could represent that person.

Mean and median are two of the measures of central tendency. They are numbers that give you information about a group of scores. This is important, because you can't very well go around reciting all the scores of a given sample whenever you need to look at or use the sample. The mean is another term for simple average. You add up all the individual scores, and then divide the sum by the number of scores. If your scores are:1, 2, 3, 6, 8, 14, and 90 then you take their sum, 124, and divide it by 7, the number of scores. You get 17.71, the mean. The mean takes into account the value of every single score. This means that the value of every single score "pulls" the mean toward itself. If any value changes, the mean changes. The median is the score that divides the collection of scores in such a way that half the scores are smaller, and half the scores are larger. Using the same scores above, (they have to be in order) you see that 6, the middle score, divides the group of scores in this way. Three scores are lower, and three are higher. So 6 is the median score. When you have an even number of scores, go half-way between the two middle-most scores. You can see that you could change the actual values of the scores in any number of ways, and still have the same median. This may seem odd, but there may be times when you want your 'estimate' of the population value to be "higher than correct" no more often than it is "lower than correct".

Because we usually don't have access to the entire population. How could you find every person in the United States and ask them if they like chocolate?

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well a sample size can be any size depending on the requirements. A sample size could be 10 people of that entire population or it could be 1000 people.

The population consists of every possible unit where a sample is a subset of the population. Note that population and sample need not refer to persons. For example, if studying biodiversity, the population could consist of plots of land.

Yes. You could have a biased sample. Its distribution would not necessarily match the distribution of the parent population.

A probability sample is one in which each member of the population has the same probability of being included. An alternative and equivalent definition is that it is a sample such that the probability of selecting that particular sample is the same for all samples of that size which could be drawn from the population.

Sample is subset of the population so sample size and population size is different.However, as a subset can be the whole set, if the sample size equals the population size, you have sampled the entire population and you will be 100% accurate with your results; it may cost much more than surveying a [representative] sample, but you get the satisfaction of knowing for what you surveyed the population exactly.Using a sample is a trade off between the cost of surveying the whole population and accuracy of the result.A census is a survey of the whole population and could be considered that the sample size = population size; in this case the results are 100% accurate.The television viewing figures are calculated using a sample of the whole population and then extrapolating them to the whole population; depending upon how the same was chosen, including its size, will affect the accuracy of the results - most likely not more than 95% accurate.With a carefully selected (that is properly biased) sample you can prove almost anything!

Population refers to all the individuals or items of interest in a particular group. Statistical population refers to the theoretical concept of all possible individuals or items that could be included in a study, from which a sample is actually drawn. Statistical population is typically larger than the actual population being studied.

If I take 10 items (a small sample) from a population and calculate the standard deviation, then I take 100 items (larger sample), and calculate the standard deviation, how will my statistics change? The smaller sample could have a higher, lower or about equal the standard deviation of the larger sample. It's also possible that the smaller sample could be, by chance, closer to the standard deviation of the population. However, A properly taken larger sample will, in general, be a more reliable estimate of the standard deviation of the population than a smaller one. There are mathematical equations to show this, that in the long run, larger samples provide better estimates. This is generally but not always true. If your population is changing as you are collecting data, then a very large sample may not be representative as it takes time to collect.

A standard deviation for a sample makes a judgment on the whole data set whereas the population standard deviation uses the shole data set. If the questions says for example, a sample of 50 peoples height was taken... you would use the sample method but if you were asked : "Everyone in the class had their height measured" you could use the population method Hope that helps

Sampling distribution is the probability distribution of a given sample statistic. For example, the sample mean. We could take many samples of size k and look at the mean of each of those. The means would form a distribution and that distribution has a mean, a variance and standard deviation. Now the population only has one mean, so we can't do this. Population distribution can refer to how some quality of the population is distributed among the population.

We could answer that in a snap, if we knew something about the population and could see the list of proposed samples. Call me crazy, but I'm suspecting wherever you copied the question from, all that stuff was right there with it.

The quickest bowlers get up to low 90's mph