0
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.
Wiki User
∙ 15y ago
Add your answer:
Earn +20 pts
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
Why don't psychologists use random samples often?
It is impossible to obtain a truly random sample. Psychologists will endeavour however to have a sample as random as is possible given the constraints of the study. Indeed there are often factors that make it difficult to obtain randomness, for example geographic location. So to answer your question, it is not that psychologists avoid the random sample, in fact, they prefer it when it is obtainable however this is often not the case.
What type of research participant should researchers use for studies of cause-and -effect relationship?
random sample
What type of research participants should researchers use for studies of cause-and-effect relationships?
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.
