In statistics, stratified sampling is a method of sampling from a population.
In statistical surveys, when subpopulations within an overall population vary, it is advantageous to
sample each subpopulation (stratum) independently. Stratification is the process of dividing
members of the population into homogeneous subgroups before sampling. The strata should be
mutually exclusive: every element in the population must be assigned to only one subpopulation.
The subpops should also be collectively exhaustive: no population element can be excluded.
Then simple random sampling or systematic sampling is applied within each stratum. This often
improves the representativeness of the sample by reducing sampling error. It can produce
a weighted mean that has less variability than the arithmetic mean of a simple random sample of the
Snowball sampling is a non-probability sampling technique where existing study subjects recruit
future subjects from among their acquaintances. Thus the sample group appears to grow like a
rolling snowball. As the sample builds up, enough data is gathered to be useful for research. This
sampling technique is often used in hidden populations which are difficult for researchers to access;
example populations would be drug users or sex workers. As sample members are not selected from
a sampling frame, snowball samples are subject to numerous biases. For example, people who have
many friends are more likely to be recruited into the sample.
The easy mnemonic to remember is that the dependent variable depends on the independent variable.
Independent variables are the things you change in your experiment. In your example that would be the
solution the egg is in.
Dependent variables are the things you measure that change in response to what you have done (the
independent variables). Generally, the dependent variables are the things you are measuring. In your
example they would be your observations. For instance, you might measure the thickness of the egg shell
3 days after exposure. Then 'shell thickness' would be your dependent variable, as it DEPENDS on the
solution you put the egg in.
Regression to the mean is the technical term for things evening out. Specifically, it refers to the
tendency of a random variable that is highly distinct from the norm to return to "normal."
For example, if a researcher gave a large group of people a test of some sort and selected the top-
performing 5%, these people would be likely to score worse, on average, if re-tested. Similarly, the bottom