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Model-based assumptions. These include the following three types: Distributional assumptions. Where a statistical model involves terms relating to random errors, assumptions may be made about the probability distribution of these errors. [5] In some cases, the distributional assumption relates to the observations themselves. Structural assumptions.
3. The logarithm of the expected value of the response variable is a linear combination of the explanatory variables. This assumption is so fundamental that it is rarely mentioned, but like most linearity assumptions, it is rarely exact and often simply made to obtain a tractable model. Additionally, data should always be categorical.
Indirect observation can be used if one wishes to be entirely unobtrusive in their observation method. This can often be useful if a researcher is approaching a particularly sensitive topic that would be likely to elicit reactivity in the subject. There are also potential ethical concerns that are avoided by using the indirect observational method.
Strong and weak sampling are two sampling approach [1] in Statistics, and are popular in computational cognitive science and language learning. [2] In strong sampling, it is assumed that the data are intentionally generated as positive examples of a concept, [3] while in weak sampling, it is assumed that the data are generated without any restrictions.
Observation is critical to scientific research and activity, and as such, observer bias may be as well. [4] When such biases exist, scientific studies can result in an over- or underestimation of what is true and accurate, which compromises the validity of the findings and results of the study, even if all other designs and procedures in the ...
In statistical hypothesis testing, a two-sample test is a test performed on the data of two random samples, each independently obtained from a different given population. The purpose of the test is to determine whether the difference between these two populations is statistically significant .
The main characteristic of exact methods is that statistical tests and confidence intervals are based on exact probability statements that are valid for any sample size. Exact statistical methods help avoid some of the unreasonable assumptions of traditional statistical methods, such as the assumption of equal variances in classical ANOVA.
That is, the two sample t-test is a test of the hypothesis that two population means are equal. The one factor ANOVA tests the hypothesis that k population means are equal. The standard ANOVA assumes that the errors (i.e., residuals) are normally distributed. If this normality assumption is not valid, an alternative is to use a non-parametric test.