Reliability allows you to assess the degree of consistency in your results. So, even if there is a minor difference in the outcomes, as long as it is within the error margin, your results are reliable. But of course, reliability doesn’t mean your outcome will be the same, it just means it will be in the same range.įor example, if you scored 95% on a test the first time and the next you score, 96%, your results are reliable. When a measurement is consistent it’s reliable. Read: Internal Validity in Research: Definition, Threats, Examples What is Reliability? In this article, we’ll look at how to assess data reliability and validity, as well as how to apply it.
If one of the measurement parameters, such as your scale, is distorted, the results will be consistent but invalid.ĭata must be consistent and accurate to be used to draw useful conclusions. So, while reliability and validity are intertwined, they are not synonymous. This means that if the standard weight for a cup of rice is 5 grams, and you measure a cup of rice, it should be 5 grams. The validity, on the other hand, refers to the measurement’s accuracy. For example, if you measure a cup of rice three times, and you get the same result each time, that result is reliable. When it comes to data analysis, reliability refers to how easily replicable an outcome is. However, in research and testing, reliability and validity are not the same things. In everyday life, we probably use reliability to describe how something is valid.
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