Standardized Tests
Standardized Tests - Related Videos
01:19
Testing a Claim about Standard Deviation
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2023
A complete procedure to test a claim about population standard deviation or population variance is explained here. The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution. As a first step, the hypothesis (null and alternative) concerning the claim about...
07:29
A Standardized Protocol for Preference Testing to Assess Fish Welfare
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Cited by 9 •
2020
A fundamental aspect of assessing the welfare of animals in captivity is to ask whether the animals have what they want. Here, we present a protocol to determine housing preference in the zebrafish (Danio rerio) with respect to the presence/absence of environmental enrichment and access to flowing of water.
14:09
Tilt Testing with Combined Lower Body Negative Pressure: a "Gold Standard" for Measuring Orthostatic Tolerance
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Cited by 31 •
2013
We describe a "gold standard" for evaluating orthostatic tolerance (OT) using tilt testing with combined lower body negative pressure (LBNP). This can be combined with non-invasive evaluations of cardiovascular reflex control. Normal and abnormal responses are defined.
07:22
Standardized Method for Measuring Collection Efficiency from Wipe-sampling of Trace Explosives
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Cited by 7 •
2017
Optimized sampling protocols and the development of new wipe materials can be facilitated by standardized measurements of collection efficiency from wipe-sampling. Our approach for sampling trace explosives uses an automated device to control speed, force, and distance during wipe-sampling followed by extraction of collected explosives.
08:44
Fabrication and Testing of Photonic Thermometers
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Cited by 1 •
2018
We describe the process of fabrication and testing of photonic thermometers.
FAQs
Reliability concerns whether a test produces consistent results, whereas validity concerns whether the results support the intended interpretation. A measure may be reliable without accurately representing the behavioral characteristic under study. Researchers therefore consider both qualities before comparing participants, assessing behavioral differences, or drawing conclusions about cognitive abilities, personality traits, or observable performance.
Norms provide a basis for interpreting an individual or group score relative to a relevant population. Their usefulness depends on how closely the comparison group matches the population and context being studied. Without appropriate norms, researchers or practitioners may misinterpret behavioral differences, particularly when applying results across different groups or circumstances.
Uniform instructions, response formats, and scoring procedures reduce differences caused by test administration rather than by the behavior being measured. Consistent conditions make comparisons across individuals, groups, or time more meaningful. If procedures vary, observed score differences may reflect changes in administration instead of genuine differences in performance, traits, or behavior.
Researchers first select a test that matches the behavioral construct and study population, then administer it under the specified conditions. They apply the predetermined scoring procedure, examine reliability, validity, and relevant norms, and interpret the results within the study context. This workflow supports cautious comparisons and reduces the risk of overinterpreting scores.
Researchers use these assessments to quantify behavioral differences, measure observable performance, examine cognitive abilities or personality traits, and investigate patterns of behavior. They can also analyze relationships between test results and other variables. The appropriate use depends on whether the test’s measurement qualities, norms, and interpretation fit the population and research question.
A test can provide a structured outcome measure before and after an intervention, allowing researchers to examine whether scores change over time. Interpretation requires attention to reliability, validity, scoring procedures, and the study context. When those features are appropriate, results can contribute evidence about intervention-related changes in performance, traits, or behavior.