Data Interpretation

Data InterpretationReliability in research methodology refers to the degree in which the same solution can be achieved using the same instruments when solving the same challenge more than a single time. High levels of reliability would, therefore, mean that you can find the same results, using the same methods when the conditions for the research is the same throughout.

Validity, on the other hand, is the extent to which the conditions that must be followed as per the scientific research method guidelines, have been adhered to by the researcher. According to Cohen et al., validity is a necessary tool for all types of academic work (2002). Validity is divided into many types like internal, content, face, external, criterion-related, construct and concurrent. Like reliability, it depends on the timescale of the work, methodology chosen, suitability of the sample under study and goodwill of the respondents to make their own choices.

In research, some errors are impossible to avoid. The two mistakes that happen mostly are the systematic and random error. Random errors are statistical variations that occur, in either mean direction in the data that is measured, as a result of limitations of precision in the measuring device. They are usually unpredictable. Systematic errors, on the contrary, are inaccuracies that happen consistently in the same way and are reproduced every time. They are often due to challenges that occur throughout the research experiment. Random error is usually a result of reliability while validity influences systematic errors.

Both of the errors interfere with the accuracy and trueness of the measured data. Random error is reduced by increased sample size while systematic error, though impossible to eliminate, may be reduced by reducing the sample size.

References
Cohen, L., Manion, L., & Morrison, K. (2002). Research methods in education. Routledge.