Assess Data Quality

To decide if you are looking for credible data from an experiment, look for the following characteristics:
✓ Reliability:
Reliability is to collect reliable data repeatable results in the following sub-measurements. If your doctor will check your weight once and you get on the scale once again and see that the dif-sharing, there is a confidence issue. Both have blood tests, blood pressure and temperature measurements and the like. It is important to use an experiment to help properly calibrated measuring instruments provide reliable data.
✓ Purity:
 Recommendations data does not contain a systematic favoring of certain people or reactions. Bias is caused in many ways: through a bad instrument, such as a scale that sometimes exceed five pounds; a bad example, as a drug study performed in adults when the drug was actually taken by children; or by researchers with preconceived expectations for the results ( "You feel better now after you took that medication is not it?")
Bias is difficult, and sometimes impossible, to measure. The best thing you can do is to predict potential problems and design your experiment to minimize them. For example, a double-blind experiment means that neither the researchers nor the patients through treatment or those in the control group. It is a way to reduce the distortion due to people on both sides.
✓ Validity: 
Valid data measure what they are intended to measure.
 For example, 
The reporting of the prevalence of crime with the number of crimes in an area that is not valid; the crime rate (number of crimes per capita) is used, because the factors in how many people live in the area.