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Checking software against expectations
Software testing is the act of checking whether software meets its intended objectives and satisfies expectations. Software testing can provide objective
Software_testing
Software testing technique that tests programs with random inputs
Random testing is a black-box software testing technique where programs are tested by generating random, independent inputs. Results of the output are
Random_testing
Data evaluation test
A randomness test (or test for randomness), in data evaluation, is a test used to analyze the distribution of a set of data to see whether it can be described
Randomness_test
Statistical significance test
the test is conservative, when one or both margins are random variables themselves. With large samples, a chi-squared test (or better yet, a G-test) can
Fisher's_exact_test
Family of statistical methods based on sampling of available data
Permutation tests (also re-randomization tests) for generating counterfactual samples Bootstrapping Cross validation Jackknife Permutation tests rely on resampling
Resampling_(statistics)
Apparent lack of pattern or predictability in events
In common usage, randomness is the apparent or actual lack of definite patterns or predictability in information. A random sequence of events, symbols
Randomness
Technique where the user tests the application or system by providing random inputs
In software testing, monkey testing is a technique where the user tests the application or system by providing random inputs and checking the behavior
Monkey_testing
Creating sequence of numbers that cannot be predicted
hard to use statistical tests to validate the generated random numbers. Wang and Nicol proposed a distance-based statistical testing technique that is used
Random_number_generation
Generalization of the one-dimensional normal distribution to higher dimensions
(univariate) normal distribution to higher dimensions. One definition is that a random vector is said to be k-variate normally distributed if every linear combination
Multivariate normal distribution
Multivariate_normal_distribution
Experiment methodology
A/B testing (also known as bucket testing, split-run testing or split testing) is a user-experience research method. A/B tests consist of a randomized experiment
A/B_testing
Software verification technique
Directed Automated Random Testing" by Patrice Godefroid, Nils Klarlund, and Koushik Sen. The paper "CUTE: A concolic unit testing engine for C", by Koushik
Concolic_testing
Blood glucose test for a non-fasting person
A random glucose test, also known as a random blood glucose test (RBG test) or a casual blood glucose test (CBG test) is a glucose test (test of blood
Random_glucose_test
Method of statistical inference
testing as a cookbook process. Hypothesis testing is also taught at the postgraduate level. Statisticians learn how to create good statistical test procedures
Statistical_hypothesis_test
Binary sequence
Intuitively, an algorithmically random sequence (or random sequence) is a sequence of binary digits that appears random to any algorithm running on a (prefix-free
Algorithmically random sequence
Algorithmically_random_sequence
Statistical interpretation with many tests
multiplicity or multiple testing problem occurs when many statistical tests are performed on the same dataset. Each test has its own chance of a Type
Multiple_comparisons_problem
Automated software testing technique
development, fuzzing or fuzz testing is an automated software testing technique that involves providing invalid, unexpected, or random data as inputs to a computer
Fuzzing
Variable representing a random phenomenon
A random variable (also called random quantity, aleatory variable, or stochastic variable) is a mathematical formalization of a quantity or object which
Random_variable
Collection of statistical models
hypothesis testing, the partitioning of sums of squares, experimental techniques and the additive model. Laplace was performing hypothesis testing in the
Analysis_of_variance
Type of functional verification unit for hardware design
monitors may be used to verify that the generator is properly testing the design. Random test generators range in scope from simple scripts and parameterized
Random_test_generator
Statistical hypothesis test
multiple samples, are significantly different. The test calculates a statistic, represented by the random variable F, and checks if it follows an F-distribution
F-test
Quality of a numerical sequence of having no recognizable patterns
appear "random" under testing have later been discovered to be very non-random when subjected to certain types of tests. The notion of quasi-random numbers
Statistical_randomness
overall. In the NCAA, players are subject to random testing with 48 hours notice, and are also randomly tested throughout the annual bowl games. The NCAA
Doping_in_American_football
Nonparametric test of the null hypothesis
hypothesis that randomly selected values X and Y from two populations have the same distribution. The value of U calculated by the test can be converted
Mann–Whitney_U_test
Statistical hypothesis test
hypothesis is true. Test statistics that follow a χ2 distribution occur when the observations are independent. There are also χ2 tests for testing the null hypothesis
Chi-squared_test
Exact statistical hypothesis test
A permutation test (also called re-randomization test or shuffle test) is an exact statistical hypothesis test. A permutation test involves two or more
Permutation_test
Statistical test
null hypothesis that the 55 test takers are comparable to a simple random sample from the population of test-takers. The Z-test tells us that the 55 students
Z-test
Class of stochastic process
stationary process where the sample space is also discrete (so that the random variable may take one of N {\displaystyle N} possible values) is a Bernoulli
Stationary_process
Statistical hypothesis test
one-sample Student's t-test is a location test of whether the mean of a population has a value specified in a null hypothesis. In testing the null hypothesis
Student's_t-test
Selection of data points in statistics
calculating the results. Random sampling error: Random variation in the results due to the elements in the sample being selected at random. Non-sampling errors
Sampling_(statistics)
Collection of utilities for empirical randomness testing
TestU01 is a software library, implemented in the ANSI C language, that offers a collection of utilities for the empirical randomness testing of random
TestU01
Process of making something random
Randomization is the process of making something random. Randomization is not haphazard; instead, a random process is a sequence of random variables describing
Randomization
Mathematical function for the probability a given outcome occurs in an experiment
distribution describes how probabilities are assigned to the possible results of a random phenomenon—more precisely, to events, which are sets of possible outcomes
Probability_distribution
Sequence where any order is equally likely
A random permutation is a sequence where any order of its items is equally likely at random, that is, it is a permutation-valued random variable of a
Random_permutation
Measure of variation in statistics
standard deviations, and 99.7% within three. The standard deviation of a random variable, sample, statistical population, data set or probability distribution
Standard_deviation
Statistical measure of how far values spread from their average
statistics, statistical inference, hypothesis testing, goodness of fit, and Monte Carlo sampling. The variance of a random variable X {\displaystyle X} is the expected
Variance
hypothesis tests about discrete probability distributions. A statistical test making use of a randomized decision rule is called a randomized test. Let D
Randomised_decision_rule
Technical analysis of a biological specimen
substance by test type. Urine analysis is primarily used because of its low cost. Urine drug testing is one of the most common testing methods used.
Drug_test
Branch of statistics
of studies, especially with the design of randomized experiments and with the planning of surveys using random sampling. The initial analysis of the data
Mathematical_statistics
Statistical test that compares goodness of fit
likelihood-ratio test, also known as Wilks test, is the oldest of the three classical approaches to hypothesis testing, together with the Lagrange multiplier test and
Likelihood-ratio_test
Sampling from a population which can be partitioned into subpopulations
stratum. Then sampling is done in each stratum, for example: by simple random sampling. The objective is to improve the precision of the sample by reducing
Stratified_sampling
Metric for fit of statistical models
hypothesis testing, e.g. to test for normality of residuals, to test whether two samples are drawn from identical distributions (see Kolmogorov–Smirnov test),
Goodness_of_fit
Form of scientific experiment
A randomized controlled trial (RCT) is a type of statistical experiment designed to evaluate the efficacy or safety of an intervention by minimizing bias
Randomized_controlled_trial
Measure of linear correlation
comparison of the strength of the joint association between different pairs of random variables that do not necessarily have the same units. As with covariance
Pearson correlation coefficient
Pearson_correlation_coefficient
Statistical property
In statistics, a sequence of random variables is homoscedastic (/ˌhoʊmoʊskəˈdæstɪk/) if all its random variables have the same finite variance; this is
Homoscedasticity and heteroscedasticity
Homoscedasticity_and_heteroscedasticity
Statistical test used on paired nominal data
exact test is an exact alternative to McNemar's test. The Stuart–Maxwell test is different generalization of the McNemar test, used for testing marginal
McNemar's_test
Ways of computing statistical significance
In statistical significance testing, a one-tailed test and a two-tailed test are alternative ways of computing the statistical significance of a parameter
One-_and_two-tailed_tests
Test of normality in frequentist statistics
values of the order statistics of independent and identically distributed random variables sampled from the standard normal distribution; finally, V {\displaystyle
Shapiro–Wilk_test
Statistical test comparing two probability distributions
samples. The Kolmogorov–Smirnov test can be modified to serve as a goodness of fit test. In the special case of testing for normality of the distribution
Kolmogorov–Smirnov_test
Class of statistical tests
normality tests are used to determine if a data set is well-modeled by a normal distribution and to compute how likely it is for a random variable underlying
Normality_test
Process involving chance used in research for allocating experimental subjects to groups
Random assignment or random placement is an experimental technique for assigning human participants or animal subjects to different groups in an experiment
Random_assignment
Dividing things between two categories
number of classes. Typical binary classification problems include: Medical testing to determine if a patient has a certain disease or not; Quality control
Binary_classification
does not look random, but it satisfies the definition of random variable. This is useful because it puts deterministic variables and random variables in
List of probability distributions
List_of_probability_distributions
Probabilistic problem-solving algorithm
methods do not always require truly random numbers to be useful (although, for some applications such as primality testing, unpredictability is vital). Many
Monte_Carlo_method
How many standard deviations apart from the mean an observed datum is
made. The z-score is often used in the z-test in standardized testing – the analog of the Student's t-test for a population whose parameters are known
Standard_score
Statistic of a given score
the test taker's "true" percentile rank probably occurs. The "true" value refers to the rank the test taker would obtain if there were no random errors
Percentile_rank
Time series statistical test
model can be estimated, and testing for a unit root is equivalent to testing δ = 0 {\displaystyle \delta =0} . Since the test is done over the residual
Dickey–Fuller_test
Statistical concept
conclusions from research: Missing completely at random, missing at random, and missing not at random. Missing data can be handled similarly as censored
Missing_data
Statistical hypothesis test
The Wilcoxon signed-rank test is a non-parametric rank test for statistical hypothesis testing used either to test the location of a population based
Wilcoxon_signed-rank_test
Quantity that indexes a parametrized family of probability distributions
forms of testing of manufactured products, rather than destructively testing all products, only a sample of products are tested. Such tests gather statistics
Statistical_parameter
Complete set of items that share at least one property in common
under consideration is modelled by a random variable, the population mean refers to the expected value of that random variable. Not every probability distribution
Statistical_population
Statistical model validation technique
against which the model is tested (called the validation dataset or testing set). The goal of cross-validation is to test the model's ability to predict
Cross-validation_(statistics)
Fundamental theorem in probability theory and statistics
{\displaystyle {\bar {X}}_{n}} denote the sample mean (which is itself a random variable). Then the limit as n → ∞ {\displaystyle n\to \infty } of the distribution
Central_limit_theorem
Study of collection and analysis of data
deals with the analysis of random phenomena. A standard statistical procedure involves the collection of data leading to a test of the relationship between
Statistics
Statistical test
testing randomness at each distinct lag, it tests the "overall" randomness based on a number of lags, and is therefore a portmanteau test. This test is
Ljung–Box_test
Statistical hypothesis test for forecasting
claimed in 1977, "temporally related". Rather than testing whether X causes Y, the Granger causality tests whether X forecasts Y. A time series X is said
Granger_causality
Empirical interventional study
treatment condition but use some criteria other than random assignment (e.g., a cutoff score on a reading test) to determine which participants are placed in
Quasi-experiment
Process of using data analysis for predicting population data from sample data
statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data
Statistical_inference
2007 document about doping in the MLB
MLB markedly increased testing and punishments. Now baseball tests unannounced twice a year for all players and random testing still occurs for selected
Mitchell_Report
Statistical property
error on the mean may be derived from the variance of a sum of independent random variables, given the definition of variance and some properties thereof
Standard_error
Function of the observed sample results
In null-hypothesis significance testing, the p-value is the probability of obtaining test results at least as extreme as the result actually observed
P-value
the Uppaal model checker and the Verimag IF toolset as well as the random testing of real-time components using a discrete event simulator. DREAM is developed
DREAM_(software)
Graphical representation of the distribution of numerical data
of bins is motivated by maximizing the power of a Pearson chi-squared test testing whether the bins do contain equal numbers of samples. More specifically
Histogram
Statistical phenomenon
of a random variable is extreme, the next sampling of the same random variable is likely to be closer to its mean. Furthermore, when many random variables
Regression_toward_the_mean
Study of health and disease within a population
epidemiology contains three case types: randomized controlled trials (often used for a new medicine or drug testing), field trials (conducted on those at
Epidemiology
Type of functions designed for being unsolvable by root-finding algorithms
There are also standards for statistical testing of new CSPRNG designs: A Statistical Test Suite for Random and Pseudorandom Number Generators, NIST Special
Cryptographically secure pseudorandom number generator
Cryptographically_secure_pseudorandom_number_generator
Range to estimate an unknown parameter
data from a random sample. Because the sample is random, the interval endpoints are random variables. Let X {\displaystyle X} be a random sample from
Confidence_interval
Algorithmically generated data that have a similar distribution as sampled data
data having one of several types of graph structure: random graphs that are generated by some random process; lattice graphs having a ring structure; lattice
Synthetic_data
Diagnostic plot of binary classifier ability
rates). An intuitive example of random guessing is a decision by flipping coins. As the size of the sample increases, a random classifier's ROC point tends
Receiver operating characteristic
Receiver_operating_characteristic
Method of statistical sampling
In statistics, stratified randomization is a method of sampling which first stratifies the whole study population into subgroups with same attributes
Stratified_randomization
Interpretation of probability
the tools of classical inferential statistics (significance testing, hypothesis testing and confidence intervals) all based on frequentist probability
Frequentist_probability
Test used in the analysis of stratified or matched categorical data
P. (September 1979). "Testing hypotheses in case-control studies-equivalence of Mantel–Haenszel statistics and logit score tests". Biometrics. 35 (3):
Cochran–Mantel–Haenszel statistics
Cochran–Mantel–Haenszel_statistics
Software licensed to be freely used, modified and distributed
robustness of MacOS applications using random testing" (PDF). Proceedings of the 1st international workshop on Random testing – RT '06. New York, New York, USA:
Free_software
Type of shift register in computing
are used in circuit testing for test-pattern generation (for exhaustive testing, pseudo-random testing or pseudo-exhaustive testing) and for signature
Linear-feedback shift register
Linear-feedback_shift_register
Computer science professor
randomness from recursive randomness. He also invented a distance based statistical testing technique to improve NIST SP800-22 testing in randomness tests
Yongge_Wang
Battery of statistical tests
The diehard tests are a battery of statistical tests for measuring the quality of a random number generator (RNG). They were developed by George Marsaglia
Diehard_tests
Topic in computer science
essentially a proof that can be verified by a property testing algorithm. Formally, a property testing algorithm with query complexity q(n) and proximity
Property_testing
Model for generating observable data in probability and statistics
variable X and target variable Y; A generative model can be used to "generate" random instances (outcomes) of an observation x. A discriminative model is a model
Generative_model
Concept in machine learning
Song Mei; Andrea Montanari (April 2022). "The Generalization Error of Random Features Regression: Precise Asymptotics and the Double Descent Curve".
Double_descent
Statistical test with teststatistic the number of signs of one type
test can be used to test the hypothesis that the difference between the X and Y has zero median, assuming continuous distributions of the two random variables
Sign_test
Non-parametric method for testing whether samples originate from the same distribution
Wallis), or one-way ANOVA on ranks is a non-parametric statistical test for testing whether samples originate from the same distribution. It is used for
Kruskal–Wallis_test
Middle quantile of a data set or probability distribution
occurs in the setting where we seek to estimate a random variable X {\displaystyle X} from a random variable Y {\displaystyle Y} , which is a noisy version
Median
Overview of and topical guide to statistics
testing Null hypothesis Alternative hypothesis P-value Significance level Statistical power Type I and type II errors Likelihood-ratio test Wald test
Outline_of_statistics
Measure of the joint variability
and statistics, covariance is a measure of the joint variability of two random variables. The sign of the covariance shows the tendency in the linear relationship
Covariance
In the design of experiments, completely randomized designs are for studying the effects of one primary factor without the need to take other nuisance
Completely_randomized_design
Statistical test
multiplier test and the likelihood-ratio test, the Wald test is one of three classical approaches to hypothesis testing. An advantage of the Wald test over
Wald_test
Statistical relationship
statistics, correlation is a type of statistical relationship between two random variables or bivariate data. It usually refers to the extent to which a
Correlation
Experiment using randomness in some aspect, usually to aid in removal of bias
acyclic graph.[needs update] A/B testing Allocation concealment Random assignment Randomized block design Randomized controlled trial Schulz KF, Altman
Randomized_experiment
Theory and technique of psychological measurement
measurement, and fairness in testing. The book also establishes standards related to testing operations—including test design and development, scores
Psychometrics
Statistical method
possible to estimate the average treatment effect in environments where random assignment to conditions is unfeasible. True causal inference using RDDs
Regression discontinuity design
Regression_discontinuity_design
Statistical methods for comparing samples
testing A/B testing Pearson's chi-squared test (2×2 tables) McNemar's test Location test Confidence interval for Youden's J statistic Hypothesis Test:
Two-proportion_Z-test
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