1. History: This involves any external events that take place during the experiment that have an effect on outcomes. For example, an earthquake occurring in the middle of a survey might produce higher levels of anxiety amongst participants and thus affect their responses. 2. Maturation: This concerns natural changes in participants which occur over time due to biological or psychological factors such as development or learning effects – for instance if people become better at completing a task after repeated attempts then this could confound results between different experimental groups. 3. Testing: The act of measuring something itself can influence the outcome so it is important to ensure tests are appropriate and reliable measures of whatever phenomenon they are designed to measure otherwise any differences found may not be attributable solely to experimental conditions but also due incorrect instruments being used; 4. Instrumentation: Variations in accuracy or reliability from one piece of equipment or measurement technique used compared another can again distort conclusions drawn from comparisons across groups; 5. Statistical Regression: When extreme scores skew results towards average it can make findings appear more significant than they actually are when applying quantitative analysis techniques – therefore outliers must be carefully checked against interpretation; 6. Selection Bias: If samples used represent biased selection (e.g.: only certain demographic groups) then this will naturally lead towards skewed outcomes and invalidate study by introducing additional confounding variables into equation (Bryman & Bell 2007). By understanding these threats researchers can better evaluate their designs before initiating experiments helping avoid similar issues arising later down track during data collection/analysis stages ensuring quality data collected interpreted correctly according standards professional practice set out within field research methodology - ultimately providing secure basis report findings accurately without risk having questioned terms their validity

Sample Solution

Internal validity is the extent to which a study’s results can be attributed to its design and methods, as opposed to potentially extraneous factors. There are six main threats to internal validity: history, maturation, testing, instrumentation, statistical regression and selection bias.