1. Calibration Involves fixing known points and constructing a scale between these fixed points. Causal Link A change in one variable that results from, or is caused by a change in another.

2. Confounding (or control) variable May affect the outcome of the investigation therefore must be kept constant. When it is not possible to keep these variables constant they should be monitored. Control experiment That which is set up to eliminate the effect of other factors affecting results. Sometimes something other than the independent variable might have produced the results obtained. A control experiment will eliminate this possibility.

3. Control group A group that is treated in EXACTLY THE SAME was except for the factor being investigated. This allows COMPARISON and ensures that data that is collected is valid. (the control group should not know they have been given a different treatment. Correleation Shows that there is a relationship between two variables, however, this might not be a causal one.

4. AccuracyAn accurate measurement that is close to the true value. Anomalous data Data which falls outside the normal or expected range of values. If you carry out more repeats it will enable you to spot anomalous data.

5. Placebo A dummy pill given to members of a control group in medical trials. Ensures that the data collected from the trial is valid. Precision Related to the smallest scale division on the measuring instrument that is being used.

6. Random distribution One that arises as a result of chance. Reliability If it can be repeated and the same results obtained. This can be improved by carrying out repeat readings and removing anomalies. If carrying out repeats in ensures that data is relaible only if you have taken anomolies into account.

7. Systematic errors The readings are all shifted in one direction from the true value. May occur when a wrongly calibrated instrument is used. Standardise Make sure each trial is the same except the independent variable. E.g. the same length of time, the same concentration of solution.

8. Evaluate a conclusion -Which points or data support the conclusion been made? -Which points of data contradict the conclusion. -What is the sample sixe like: good/bad? -Might other factors be having an effect? Representative data There are many factors that could cause regional or group variation e.g. geographical location or sex. Trying a test out on a wide avriety of individuals will ensure that the data is representative.

9. Line of best fit Shoes the pattern in your data. A line of best fit should only be drawn when you CANNOT PREDICT the intermediate values. Percentage increase Allows results to be compared particularly if all subjects/trails started off with different starting measurements.

10. Ethical studies No harmful effects. No change to normal lifestyle Participants are volunteers Participants were healthy. g/kg of body mass When using this to calculate amounts of drugs administered it takes into starting mass but also allows comparison between participants.

11. Buffer A solution that maontains the pH. Useful in enzyme experiments.