<P> The term "observational error" is also sometimes used to refer to response errors and some other types of non-sampling error . In survey - type situations, these errors can be mistakes in the collection of data, including both the incorrect recording of a response and the correct recording of a respondent's inaccurate response . These sources of non-sampling error are discussed in Salant and Dillman (1995) and Bland and Altman (1996). </P> <P> These errors can be random or systematic . Random errors are caused by unintended mistakes by respondents, interviewers and / or coders . Systematic error can occur if there is a systematic reaction of the respondents to the method used to formulate the survey question . Thus, the exact formulation of a survey question is crucial, since it affects the level of measurement error (). Different tools are available for the researchers to help them decide about this exact formulation of their questions, for instance estimating the quality of a question using MTMM experiments or predicting this quality using the Survey Quality Predictor software (SQP). This information about the quality can also be used in order to correct for measurement error () </P> <P> If the dependent variable in a regression is measured with error, regression analysis and associated hypothesis testing are unaffected, except that the R will be lower than it would be with perfect measurement . </P> <P> However, if one or more independent variables is measured with error, then the regression coefficients and standard hypothesis tests are invalid . </P>

How to reduce effect of random error in titration