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2. Plackett–Burman design - Wikipedia

   en.wikipedia.org/wiki/Plackett–Burman_design

   Plackett–Burman designs are experimental designs presented in 1946 by Robin L. Plackett and J. P. Burman while working in the British Ministry of Supply. [1] Their goal was to find experimental designs for investigating the dependence of some measured quantity on a number of independent variables (factors), each taking L levels, in such a way as to minimize the variance of the estimates of ...

3. Barnard's test - Wikipedia

   en.wikipedia.org/wiki/Barnard's_test

   Under specious pressure from Fisher, Barnard retracted his test in a published paper, [8] however many researchers prefer Barnard’s exact test over Fisher's exact test for analyzing 2 × 2 contingency tables, [9] since its statistics are more powerful for the vast majority of experimental designs, whereas Fisher’s exact test statistics are conservative, meaning the significance shown by ...

4. Experimental uncertainty analysis - Wikipedia

   en.wikipedia.org/wiki/Experimental_uncertainty...

   To illustrate, a simple example of this process is to find the mean and variance of the derived quantity z = x 2 where the measured quantity x is Normally distributed with mean μ and variance σ 2. The derived quantity z will have some new PDF, that can (sometimes) be found using the rules of probability calculus. [ 7 ]

5. 68–95–99.7 rule - Wikipedia

   en.wikipedia.org/wiki/68–95–99.7_rule

   Diagram showing the cumulative distribution function for the normal distribution with mean (μ) 0 and variance (σ 2) 1. These numerical values "68%, 95%, 99.7%" come from the cumulative distribution function of the normal distribution. The prediction interval for any standard score z corresponds numerically to (1 − (1 − Φ μ,σ 2 (z)) · 2).

6. Experiment (probability theory) - Wikipedia

   en.wikipedia.org/wiki/Experiment_(probability...

   In probability theory, an experiment or trial (see below) is any procedure that can be infinitely repeated and has a well-defined set of possible outcomes, known as the sample space. [1] An experiment is said to be random if it has more than one possible outcome, and deterministic if it has only one.

7. Statistical hypothesis test - Wikipedia

   en.wikipedia.org/wiki/Statistical_hypothesis_test

   The typical steps involved in performing a frequentist hypothesis test in practice are: Define a hypothesis (claim which is testable using data). Select a relevant statistical test with associated test statistic T. Derive the distribution of the test statistic under the null hypothesis from the assumptions.

8. Sensitivity analysis - Wikipedia

   en.wikipedia.org/wiki/Sensitivity_analysis

   Figure 2. Sampling-based sensitivity analysis by scatterplots. Y (vertical axis) is a function of four factors. The points in the four scatterplots are always the same though sorted differently, i.e. by Z 1, Z 2, Z 3, Z 4 in turn. Note that the abscissa is different for each plot: (−5, +5) for Z 1, (−8, +8) for Z 2, (−10, +10) for Z 3 and ...

9. Event (probability theory) - Wikipedia

   en.wikipedia.org/wiki/Event_(probability_theory)

   In probability theory, an event is a set of outcomes of an experiment (a subset of the sample space) to which a probability is assigned. [1] A single outcome may be an element of many different events, [2] and different events in an experiment are usually not equally likely, since they may include very different groups of outcomes. [3]