Experiment cannot be predicted in advance is RANDOM EXPERIMENT......
set of all possible outcomes.
outcome that can be predicted with certainity.
when an experiment performed repeatedly- called trial.
Ex. If a coin is tossed,we can't say,whetefr head or tail will appear .so it is a Random Experiment.
Sample Space:-- Possible outcomes of a random experiment..
set of all posssible outcomes..
denoted by--- "S".
and no. of elements is denoted by n(s).
ex. In throwing a dice ,the number that appears at top is any one of 1,2,3,4,5,6 ,So here:
S= 1,2,3,4,5,6 n(s) --- 6
There are circumstances when it is important and others when it is not. If, for example, you wanted a sample of all schools in the country, it would make more sense to go for cluster sampling. A lot of market research work will require quota sampling. So the supremacy of a random sample is a myth.
A random distribution is a random sample set displayed in the form of a bell curve. See random sample set.
to select a random sample you pick them at random
The sample size determines the accuracy of results in an experiment
An experimental sample is an experiment that is just a sample of what you are looking for.
Sample: The answer is called Sample space.
bias
A random variable is a function that assigns unique numerical values to all possible outcomes of a random experiment. A real valued function defined on a sample space of an experiment is also called random variable.
controlled experiment
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.
This is not a question.
The set of all possible outcomes of a random experiment is nothing but sample space usually denoted by S. we can also call it as event. For example our experiment is rolling a dice, then our sample space is S= {1,2,3,4,5,6}
random sample is a big sample and convenience sample is small sample
The answer is Random Sample
False
There are circumstances when it is important and others when it is not. If, for example, you wanted a sample of all schools in the country, it would make more sense to go for cluster sampling. A lot of market research work will require quota sampling. So the supremacy of a random sample is a myth.
You can overcome or reduce the problem of random error and systematic error while doing an experiment by increasing the sample size, which means averaging over a huge number of observations.