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Reliability refers to the consistency of a measure, indicating how stable and dependable the measure is across multiple administrations or different occasions. It's important in assessment to ensure that results are replicable and not due to random error.

Validity is the degree to which a test accurately measures what it intends to measure. In assessment, high validity is crucial to draw meaningful and accurate conclusions from the test scores.

The Standard Error of Measurement is an index indicating the amount of error inherent in an individual's observed score on a test. It's important for assessing the precision of individual test scores and establishing confidence intervals.

Internal consistency measures the extent to which items on a test measure the same construct and are correlated with one another. High internal consistency is important to ensure the test's items are assessing the same underlying trait.

Test-Retest Reliability measures the stability of test scores over time by administering the same test to the same individuals at two different points. It's key for evaluating the temporal stability of the measured construct.

Inter-Rater Reliability assesses the extent to which different raters or observers give consistent estimates of the same phenomenon. It's important when human judgment influences scoring, to ensure objectivity.

Construct Validity refers to how well a test or instrument measures the theoretical construct it's intended to measure. This form of validity is fundamental for ensuring that the test is actually assessing the intended psychological concept.

Content Validity indicates the extent to which a measure represents all facets of a given construct. It's significant for making sure that an assessment tool is fully capturing the domain it purports to measure.

Face Validity is the extent to which a test appears to measure what it is supposed to measure, based on a superficial examination of its content. While not a strict form of validity, it can affect the acceptance and motivation of test-takers.

Criterion-Related Validity assesses how well one measure predicts an outcome based on another, established criterion measure. It is important for demonstrating the practical utility of a test in predicting relevant outcomes.

Convergent Validity is the degree to which two measures that are supposed to be measuring the same construct are actually related. It's critical for clarifying that different measures of a construct are truly capturing the same thing.

Discriminant Validity is the degree to which measures of different constructs are unrelated. It's essential for confirming that distinct concepts measured by a test aren't mistakenly blending or overlapping.

Sensitivity in psychometrics refers to a test's ability to correctly identify true positives. It is crucial for ensuring that an instrument accurately detects individuals who possess the trait or condition being measured.

Specificity means a test's ability to correctly identify true negatives. It is important for ensuring that a test correctly excludes those who do not have the trait or condition in question, reducing false-positive rates.