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How do you select random samples in statistics?
To select random samples in statistics, you can use methods such as simple random sampling, systematic sampling, stratified sampling, or cluster sampling. Simple random sampling involves selecting individuals from a population where each has an equal chance of being chosen, often using random number generators. Systematic sampling selects every nth individual from a list, while stratified sampling divides the population into subgroups and samples from each. Cluster sampling involves dividing the population into clusters, then randomly selecting entire clusters to include in the sample.
How many different simple random samples of 7 are possible from a pool of 25?
There are 25C7 different samples of seven from a pool of 25.25C7 = 25!/(7!(25-7)!) = 480 700 different samples of 7
What is the differences between simple random sampling and stratified random sampling?
Simple random sampling involves selecting individuals from a population entirely by chance, ensuring that each member has an equal probability of being chosen. In contrast, stratified random sampling involves dividing the population into distinct subgroups or strata based on specific characteristics (e.g., age, gender) and then randomly selecting samples from each stratum. This method ensures that different segments of the population are adequately represented, leading to potentially more accurate and reliable results.
Why do Social Scientists gather data from samples instead of populations?
It is simple statistics. It would be almost impossible to survey an entire population, so they apply a simple random sample in which each person in the population receives a numerical value (001 - 999 for example). Then they use a random number generator on a calculator or use a random digit table to choose an appropriate amount of people to be sampled. It is more cost effective and easier to do it this way, yet still represents the population fairly accurately.
When dividing a population into subgroups so that a random sample from each subgroup can be collected?
simple random sampling
How many simple random samples of size 5 can be selected from a population of size 35?
There are 324,632 possible samples.
What is the definition of simple random sample?
There are two equivalent ways of defining a simple random sample from a larger population. One definition is that every member of the population has the same probability of being included in the sample. The second is that, if you generate all possible samples of the given size from the population, then each such sample has the same probability of being selected for use.
What is the formula for simple random sampling?
The formula for simple random sampling is: n = N * (X / M) Where: n = number of samples N = population size X = sample size chosen M = total number of units in the population
How many different simple random samples of 7 are possible from a pool of 25?
There are 25C7 different samples of seven from a pool of 25.25C7 = 25!/(7!(25-7)!) = 480 700 different samples of 7
How many different simple random samples of size 4 can be obtained from a population whose size is 42?
Number of samples = 42C4 = 42*41*40*39/24 = 111930
How many simple random samples of size 3 can be selected from a population of size 7?
7*6*5/(3*2*1) = 35
How many different simple random sample of size 7 can be obtained from a population which is a size 100?
There are 16,007,560,800 or just over 16 billion samples.
What is stratified random sampling in statistics?
Stratified Random Sampling: obtained by separating the population into mutually exclusive (only belong to one set) sets, or stratas, and then drawing simple random samples (a sample selected in a way that every possible sample with the same number of observation is equally likely to be chosen) from each stratum.
Describe how more complex probability sampling techniques could provide samples more representative of a target population than simple random sampling Illustrate your answer with a criminal justice e?
Describe how more complex probability sampling techniques could provide samples more representative of a target population than simple random sampling Illustrate your answer with an information technology example.
What is the difference between a simple random sample and a stratified random sample?
Sometimes a population consists of a number of subsets (strata) such that members within any particular strata are alike while difference between strata are more than simply random variations. In such a case, the population can be split up into strata. Then a stratified random sample consists of simple random samples, with the same sampling proportion, taken within each stratum.
How many simple random samples of size 5 can be obtained from a population whose size is 38?
You wouldn't want to use the same item twice, so just divide 38/5 = 7+ ... you can get 7 samples of size 5.
When individuals all have an equal chance of being selected and all samples have an equal chance of being selected its called a stratified random example convenience cluster or simple random sample?
Simple random sampling.
