Types of Statistical Tests
Hypothesis testing can be done via use of either one-tailed or two-tailed statistical test. The purpose of these tests is to obtain the probability with which a parameter from a given data set is statistically significant. These are also called lateral flow and dipstick tests.
- One-tailed test can be used so that the differences of the parameter estimations within only one side from a given standard can be perceived plausible.
- Two-tailed test needs to be applied in the case when you consider deviations from both sides of benchmark value as possible in science.
The expression “tail” is used in the terminology in which those tests are referred and the reason for that is that outliers, i.e. observation ended up rejecting the null hypothesis, are the extreme points of the distribution, those areas normally have a small influence or “tail off” similar to the bell shape or normal distribution. One study should make an application either the one-tailed test or two-tailed test according to the judgment of the research hypothesis.
Tests of Significance: Process, Example and Type
Test of significance is a process for comparing observed data with a claim(also called a hypothesis), the truth of which is being assessed in further analysis. Let’s learn about test of significance, null hypothesis and Significance testing below.
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