I believe you mean to say, equally probable. By stating they are separate events, I assume that they are independent and that there is a single unique outcome to each event that can be identified. Ok, then the chance of each event or outcome is 1/10.
In a probability sample, each unit has the same probability of being included in the sample. Equivalently, given a sample size, each sample of that size from the population has the same probability of being selected. This is not true for non-probability sampling.
Classical probability theory is concerned with carrying out probability calculations based on equally likely outcomes. That is, it is assumed that the sample space has been constructed in such a way that every subset of the sample space consisting of a single element has the same probability. If the sample space contains n possible outcomes (#S = n), we must have for all s 2 S, P(fsg) = 1 n and hence for all E S P(E) = #E n : More informally, we have P(E) = number of ways E can occur total number of outcomes :
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.
probably means that something or which is not sure . LIKE :- I PROBABLY GET THIS ANSWER RIGHT.
Discrete probability. It helps if the all the outcomes in the sample space are equally probable but that is not a necessity.
I believe you mean to say, equally probable. By stating they are separate events, I assume that they are independent and that there is a single unique outcome to each event that can be identified. Ok, then the chance of each event or outcome is 1/10.
No, it is not.
In a probability sample, each unit has the same probability of being included in the sample. Equivalently, given a sample size, each sample of that size from the population has the same probability of being selected. This is not true for non-probability sampling.
Classical probability theory is concerned with carrying out probability calculations based on equally likely outcomes. That is, it is assumed that the sample space has been constructed in such a way that every subset of the sample space consisting of a single element has the same probability. If the sample space contains n possible outcomes (#S = n), we must have for all s 2 S, P(fsg) = 1 n and hence for all E S P(E) = #E n : More informally, we have P(E) = number of ways E can occur total number of outcomes :
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.
probably means that something or which is not sure . LIKE :- I PROBABLY GET THIS ANSWER RIGHT.
It is quite likely that the sample is not representative of the population and so while statistical conclusion may be valid for the sample, they may not apply to the population.
In the context of a sample of size n out of a population of N, any sample of size n has the same probability of being selected. This is equivalent to the statement that any member of the population has the same probability of being included in the sample.
The key feature is that each sample of the given size has the same probability of being selected as the sample. Equivalently, each unit in the population has the same probability of being included in the sample.
Probability.
random sample or probability sample