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0.00034 has 2 significant figures (3 and 4) if the resolution is 0.00001. Zeros to the right of the last non-zero digit (trailing zeros) in a number with the decimal point are significant if they are within the measurement or reporting resolution. 1.200 has four significant figures (1, 2, 0, and 0) if they are allowed by the measurement resolution.
Thus, the probability that a number starts with the digits 3, 1, 4 (some examples are 3.14, 3.142, π, 314280.7, and 0.00314005) is log 10 (1 + 1/314) ≈ 0.00138, as in the box with the log-log graph on the right. This result can be used to find the probability that a particular digit occurs at a given position within a number.
A complex explanation is a long computer program. Simple explanations are more likely, so a high-probability observation string is one generated by a short computer program, or perhaps by any of a large number of slightly longer computer programs. A low-probability observation string is one that can only be generated by a long computer program.
for any Borel set A of real numbers with Lebesgue measure equal to zero, the probability of X being valued in A is also equal to zero; for any positive number ε there is a positive number δ such that: if A is a Borel set with Lebesgue measure less than δ, then the probability of X being valued in A is less than ε.
The statistical power of a test is the probability that it correctly rejects the null hypothesis when the null hypothesis is false. Referring to statistical significance does not necessarily mean that the overall result is significant in real world terms.
Binary variables are widely used in statistics to model the probability of a certain class or event taking place, such as the probability of a team winning, of a patient being healthy, etc. (see § Applications), and the logistic model has been the most commonly used model for binary regression since about 1970. [3]
Relative risk is commonly used to present the results of randomized controlled trials. [5] This can be problematic if the relative risk is presented without the absolute measures, such as absolute risk, or risk difference. [6]
The decline effect may occur when scientific claims receive decreasing support over time. The term was first described by parapsychologist Joseph Banks Rhine in the 1930s to describe the disappearing of extrasensory perception (ESP) of psychic experiments conducted by Rhine over the course of study or time.