The state or attribute of being dependable. 2: the extent to which a repeated trial of an experiment, test, or measuring process produces the same outcomes over time: the stability of a trait or characteristic.
Reliability is the degree to which we can trust a measurement. In statistics, reliability is the degree to which a measure is free from error. There are three main types of statistical errors: random error, systematic error, and pure noise. Random error occurs when taking multiple measurements on a single sample. These errors are added together in computing measures of central tendency (such as means) and variability (such as standard deviations). Systematic error is a type of bias that remains even after you correct for random error. It may be due to something physical associated with how the measurement device works or something psychological associated with how the person taking the measurement behaves. Pure noise is random error without any signal present. Noise can be divided into two types: white noise and brown noise.
Random error can be reduced by using multiple observers, which increases the number of samples, or by using statistical methods to adjust for observer differences. Systematic error can never be removed completely, but it can be reduced by doing additional experiments or using control groups. Pure noise can be reduced by using higher sampling rates or by using signal processing techniques such as low-pass filtering.
The consistency of the measurement is referred to as its reliability. The reliability of a test score indicates its trustworthiness. The information is credible if the obtained data yields the same findings when examined using different procedures and sample groups. Reliability is an important factor in determining the usefulness of a test.
An instrument is considered reliable if several tests give similar results. This means that any one test score cannot be used in place of another; instead, several scores must be averaged together to get an accurate picture of how someone has performed on the test. Test-retest reliability refers to the stability of a measure over time. If the same questionnaire is administered a few days later, we can assume that the person's rating will be fairly consistent across the two occasions.
Validity is the degree to which an instrument measures what it is supposed to measure. Validity refers to the extent to which an assessment tool captures all aspects of the construct being measured. It is also known as "appropriateness." The term validity is often used interchangeably with reliability, but they are not the same thing. For example, although reliability is always desirable, a test might have good reliability but be valid for the purpose for which it is being used.
The three types of validity are content validity, criterion-related validity, and construct validity.
The term "reliability" refers to the consistency of the outcomes achieved. The degree to which the researcher genuinely measures what he or she is attempting to assess is referred to as validity. Target A demonstrates a measurement with high reliability but low validity since the shots are consistent but not in the center of the target. Target B has high validity but low reliability since the shots are all over the place-some in front of, some behind, and some dead on.
In general, measurements that rely on subjective reports by participants are less reliable than those that use objective indicators. For example, someone might say that they feel confident before taking a test, yet score poorly on it because they didn't study enough. In addition, questions that measure attitudes, opinions, or behaviors tend to be unreliable because people can change their answers to fit what others expect of them. For example, if you ask someone how they feel about obesity, they might say they dislike it even though what they really want to do is lose weight for a friend's wedding next week.
Finally, measurements that depend on the judgment of the interviewer are generally considered less reliable than those that can be answered by looking at someone's behavior. For example, if the interviewer decides whether or not to give you credit based on his or her opinion of your performance, this would be considered an subjective measurement.
In conclusion, measurements that rely on subjective reports by participants or observers are less reliable than those that use objective indicators.
Repeatability The phrase "reliability" in research refers to "repeatability" or "consistency." A measure is deemed dependable if it consistently produces the same result (provided that what we are measuring does not change!). Let's look more closely at what it means to state a metric is "repeatable" or "consistent." Repeatability refers to an instrument's ability to give the same score on repeated trials. If the same researcher scores the same subjects on two different occasions, then we can say that the measure is repeatable. There are at least three ways to improve the repeatability of an instrument: use multiple observers, have each subject serve as his or her own control by being scored before and after taking the test, or divide subjects into subgroups and score them separately.
Reliability Reliability is the degree to which scores on a measurement tool are consistent over time or across raters. In reliability studies, researchers try to determine whether scores on a given tool are stable over time or not. If they are, then we can say that the tool is reliable. Tools can be reliable in use but not reliable for someone - for example, if one person uses the tool incorrectly and another person observes that misuse, then they will not be able to obtain accurate scores using the tool. Even if a tool is reliable when used by one person, this does not guarantee that it will be reliable when used by others. That depends on how much variance there is in scores when the tool is used by different people.