A value selected by the researcher to fix the probability of a Type I Error.  Denoted as , this arbitrary choice is always a small probability.  The most common choice in behavioral research is .05, while .01 is occasionally used.  The significance level determines the critical value drawn from the statistical table.

The probability of rejecting a set of assumptions when they are in fact true.

the probability of finding a relationship between your treatment and effect when there isn't one in reality.

The standard significance levels are 95% (p0.05) and 99% (p0.01).

a margin of tolerance for potential errors in concluding whether there is real change from your pre-survey to your post. Most scientists use a 5% (or .05) significance level. The more stringent your significance level, the higher the changes have to be from pre-survey to post- before you conclude that a change is real.

(α) [this is the greek letter "alpha", some browsers might not render this correctly] A preselected value which the -value must not exceed for the null hypothesis to be rejected. The significance level gives an upper bound on the probability of a type I error. It is traditionally set to be 5%.

In hypothesis testing, the significance level refers to the probability of making a Type I error, or rejecting the null hypothesis when it is actually true. The researcher decides on the level of significance for each test.

The probability with which the experimenter is willing to reject the null hypothesis ( in favour of the alternative hypothesis ) when the null hypothesis is in fact correct. Also known as the probability of a type I error.

Established at the outset by a researcher when using statistical analysis to test a hypothesis (e.g. 0.05 level or 0.01 significance level). A significance level of 0.05 indicates the probability that an observed difference or relationship would be found by chance only 5 times out of every 100 (1 out of every 100 for a significance level of 0.01). It indicates the risk of the researcher making a Type I error (i.e. an error that occurs when a researcher rejects the null hypothesis when it is true and concludes that a statistically significant relationship/difference exists when it does not). (4)

The level, set usually at 5% or 1%, below which the p-value would need to be in order to declare that a population effect exists.

the probability of obtaining the evidence if the null hypothesis were true.

The significance level of a test is the smallest alpha level at which the null hypothesis would be rejected. Usually, if the significance level is less than a number such as .05 (5%), the null hypothesis would be rejected in favor of the alternative. In many cases, the significance level can be thought of intuitively as the chance of getting a sample like the one being analyzed if the null hypothesis were true. A small significance level would imply that getting such a sample was highly unlikely, suggesting that the null hypothesis is probably not true. The significance level is also called the P value of the test.

The likelihood that the results observed from a study were due to chance – the significance level of one chance in twenty (probability or p = .05) or one chance in 100 (p = .01) is a high degree of improbability EHR/NSF Evaluation Handbook, Chapter Seven:   GlossarySource web site