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What is the difference between moderating and extraneous variables?
Extraneous variable a.k.a. Confounding vaiable is a variable that affects an independent variable n also afects a dependent variable at d same time confounding relatnship btn the independent and dependent variable. Mediating variable a.k.a. Intervening variable, it is a variable forming a link btn two variables that are causualy conected.
What are Confounding Variables?
In any experiment there are many kinds of variables that will effect the experiment. The independent variable is the manipulation for the experiment and the dependent variable is the measure you take from that experiment. Confounding variables are things in which have an effect on the dependent variable, but were taken into account in the experimental design. For example, you want to know if Drug X has an effect on causing sleep. The experimenter must take care to design the experiment so that he can be very sure that the subjects in the study fell asleep because of the influence of his Drug X, and that the sleepiness was not caused by other factors. Those other factors would be confounding variables.
What is meant by confounding in math?
In statistics a confounding variable is one which can give rise to spurious correlations. For example, my age is fairly well correlated with the number of television sets in the UK. This is not because my getting older sells more TV sets, nor is it because the sale of TV sets makes me grow older. The real reason is that both these are correlated with time and, as the years pass, both increase. So, time is the confounding variable which gives rise to an apparent relationship between TV sets and my age. Confounding variables can have serious effects when statistical methods are being used to develop a cause-and-effect model. In truth, there may be no direct causal relationship, only two independent relationships with a third variable - the confounding factor.
What third variables might there be that would cause a spurious correlation in the survey results?
The third variable could be one which is correlated to both variables. These are called confounding variable. For example, in the UK you could find a correlation between coastal air pollution and ice cream sales. This is not because eating ice cream causes air pollution nor because air pollution causes people to eat ice cream. The confounding variable is the temperature. Warm weather gets people to drive to the sea!
What is the importance of unexpected outcomes?
depends on what subject: for psychology, stress is elevated more when people are suprised with new unpredicted challenges rather than handling a known stress. In an experiment, it would mean the variables are not related and another confounding varialbe is affecting the change, statistical indifference?
