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Well, sort of. The Chi-square distribution is the sampling distribution of the variance. It is derived based on a random sample. A perfect random sample is where any value in the sample has any relationship to any other value. I would say that if the Chi-square distribution is used, then every effort should be made to make the sample as random as possible. I would also say that if the Chi-square distribution is used and the sample is clearly not a random sample, then improper conclusions may be reached.
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What is the difference between random and stratified sample in the survey method?
The main difference is that the way of selecting a sample Random sample purely on randomly selected sample,in random sample every objective has a an equal chance to get into sample but it may follow heterogeneous,to over come this problem we can use stratified Random Sample Here the difference is that random sample may follow heterogeneity and Stratified follows homogeneity
What is a stratified random sample?
Stratified random sampling is a sampling scheme which is used when the population comprises a number of strata, or subsets, which are similar within the strata but differ from one stratum to another. One example is school children stratified according to classes, or salaries stratified by departments.A simple random sample may not have enough representatives from each stratum and the solution is to use stratified random sampling. Under this scheme, the overall sampling proportion (sample size/population size) is determined and a sample is drawn from each stratum which represents the same proportion.
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.
A random sample of 120 students has a test score average with a standard deviation of 11.4 Find the margin of error if c equals 0.90?
i y=use Z-test
When every member of a set has an equal chance of being selected as part of a sample it is called what?
A simple random sample or a probability sample.
What is the difference between random and stratified sample in the survey method?
The main difference is that the way of selecting a sample Random sample purely on randomly selected sample,in random sample every objective has a an equal chance to get into sample but it may follow heterogeneous,to over come this problem we can use stratified Random Sample Here the difference is that random sample may follow heterogeneity and Stratified follows homogeneity
Does a Quota Sample represent the whole population?
yes because the quota sample include the random sample and when we have over estimation we will use the quota sample
Which kind of sample is most frequently used by social scientists?
Social scientists most often use a random sample
What type of research participant should researchers use for studies of cause-and -effect relationship?
random sample
What is the difference between stratified an random sampling?
In (Simple) random sampling, all of the units in the sample have the same chance of being included in the sample. Units are selected randomly from a population by some random method that gives equal probability to each element. In stratified random sampling, the entire population is divided into heterogeneous sub-popuation known as strata (sub-population with unequal variances) and a random sample is chosen from each of these stratum. The reason when to use which depends on the situation and need of the experimenter.
What is a stratified random sample?
Stratified random sampling is a sampling scheme which is used when the population comprises a number of strata, or subsets, which are similar within the strata but differ from one stratum to another. One example is school children stratified according to classes, or salaries stratified by departments.A simple random sample may not have enough representatives from each stratum and the solution is to use stratified random sampling. Under this scheme, the overall sampling proportion (sample size/population size) is determined and a sample is drawn from each stratum which represents the same proportion.
In a random sample of 200 people 140 are successful historically 75 of the population has been classified as successful what is the mean of the sampling distribution of the proportion?
1. In a random sample of 200 persons of a town, 120 are found to be tea drinkers. In a random sample of 500 persons of another town, 240 are found to be tea drinkers. Is the proportion of tea drinkers in the two towns equal? Use 0.01 level of significance.
How can you choose a random sample of teachers?
The first step is to establish a sampling frame. This is a list of all teachers in the domain that you are interested in. Next you allocate a different number to each teacher. Then you use a random number generator to generate random numbers. You select each teacher whose number is generated. If the teacher has already been selected for inclusion in the sample, you ignore the duplicate and continue until you have a sample of the required size.
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.
Why is it important to ensure that an experimental sample is totally random?
To ensure that the results produced from your sample are fair and true. For example, if you had to pick 10 random numbers between 1 and 6, you could just say numbers that come into your head, but that wouldn't be random because you're choosing the numbers. A more random and fair way would be to roll a die 10 times and use those numbers, because you are in no way picking the numbers.
What is the difference between stratifying and blocking?
Pretend you have a mountain of dirty clothes. You want to see the difference between washing dark and light colored clothing with warm and cold water. You need a sample because washing all of them takes too long. Stratifying means you separate the clothes into dark and light colors and you pick a simple random sample from EACH color pile (this ensures you have the same number of clothes for both colors). Then you combine them into a miniature sample and you use RANDOM ASSIGNMENT to assign the clothes in the sample to either cold or warm water treatment. Blocking also means that you separate the clothes into dark and light colors and you pick a simple random sample from each color pile. However, instead of combining it to make a miniature sample, you keep them separate and use random assignment WITHIN these blocks. Eg. for the dark color pile, you use random assignment to assign half to cold, half to warm water washing, and you repeat the same process for the light color pile, keeping them separate the entire time.
What is domain sampling in statistics?
It's a model for measuring reliability of measures of a construct. First you choose randomly a finite number of items from an infinite pool of items to measure the construct and then use it as a criterion to evaluate reliability of other chosen samples. The higher the correlation of the scores derived using any random sample with the score derived using the criterion sample, the higher the reliability of the random sample
