What is Statistical Significance?
In normal English, “significant” means important, while in Statistics “significant” means probably true (not due to chance). A research finding may be true without being important. When statisticians say a result is “highly significant” they mean it is very probably true. They do not (necessarily) mean it is highly important. Take a look at the table below. The chi (pronounced kie like pie) squares at the bottom of the table show two rows of numbers. The top row numbers of 0.07 and 24.4 are the chi square statistics themselves. The meaning of these statistics may be ignored for the purposes of this article. The second row contains values .795 and .001. These are the significance levels and are explained following the table. Significance levels show you how likely a result is due to chance. The most common level, used to mean something is good enough to be believed, is .95. This means that the finding has a 95% chance of being true. However, this value is also used in a misleading way.
Statistical significance is a mathematical tool used to determine whether the outcome of an experiment is the result of a relationship between specific factors or due to chance. Statistical significance is commonly used in the medical field to test drugs and vaccines and to determine causal factors of disease. Statistical significance is also used in the fields of psychology, environmental biology, and any other discipline that conducts research through experimentation. Statistics are the mathematical calculations of numeric sets or populations that are manipulated to produce a probability of the occurrence of an event. Statistics use a numeric sample and apply that number to an entire population. For the sake of example, we might say that 80% of all Americans drive a car. It would be difficult to question every American about whether or not they drive a car, so a random number of people would be questioned and then the data would be statistically analyzed and generalized to account fo
http://www.numberwatch.co.uk/significance.htm by HYPERLINK “http://www.ecs.soton.ac.uk/%7Ejeb/cv.htm” John Brignell Statistical significance All results obtained by statistical methods suffer from the disadvantage that they might have been caused by pure statistical accident. The level of statistical significance is determined by the probability that this has not, in fact, happened. P is an estimate of the probability that the result has occurred by statistical accident. Therefore a large value of P represents a small level of statistical significance and vice versa. In experiments where we are obliged to resort to statistics it is therefore proper procedure to define a level of significance at which a correlation will be deemed to have been proven, though the choice is often actually made after the event. It is important to realise that, however small the value of P, there is always a finite chance that the result is a pure accident. A typical level at which the threshold of P is set
