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
In a stratified sample, the sampling proportion is the same for each stratum. In a random sample it should be but, due to randomness, need not be.
Biased- (Not random) Unbiased-(Random) Example: (ubbiased) Woman takes random people to take a survey.
simple random sample is to select the sample in random method but systematic random sample is to select the sample in particular sequence (ie 1st 11th 21st 31st etc.)• Simple random sample requires that each individual is separately selected but systematic random sample does not selected separately.• In simple random sampling, for each k, each sample of size k has equal probability of being selected as a sample but it is not so in systematic random sampling.
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.
Convenience sample
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
In a stratified sample, the sampling proportion is the same for each stratum. In a random sample it should be but, due to randomness, need not be.
Biased- (Not random) Unbiased-(Random) Example: (ubbiased) Woman takes random people to take a survey.
a biased sample is chosen because it is liked more (looks prettier, more popular etc.) and a random one has no prefeerence hope i helped!!!! :p
simple random sample is to select the sample in random method but systematic random sample is to select the sample in particular sequence (ie 1st 11th 21st 31st etc.)• Simple random sample requires that each individual is separately selected but systematic random sample does not selected separately.• In simple random sampling, for each k, each sample of size k has equal probability of being selected as a sample but it is not so in systematic random sampling.
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.
Random sampling is the sample group of subjects that are selected by chance, without bias. Random assignment is when each subject of the sample has an equal chance of being in either the experimental or control group of an experiment.
Random assignment: assigning participants to experimental and control conditions by chance Vs. Random sample: a sample that fairly represents a population because each member has an equal chance of being included You decide :-D
Simple Random Sample Stratified Random Sampling Cluster Sampling Systematic Sampling Convenience Sampling
Non-probability or Judgement Samples has to do with a basic researcher assumptions about the nature of the population, the researcher assumes that any sample would be representative to the population,the results of this type of samples can not be generalized to the population(cause it may not be representative as the research assumed) and the results may be biased. Probability or Random samples is a sample that to be drawn from the population such that each element in the population has a chance to be in the selected sample the results of the random samples can be used in Statistical inference purposes
Convenience sample Systematic sample Simple random sample (SRS) Census