Question

State in your own words what is meant by Type I and Type II errors. Why are these important? Name one thing that can be done to improve internal validity of a study. only detailed and good term related with references that are research related. Thanks

Answer 1

Type I error - Error that occurs when the researcher concludes that the sample tested are from different population (a significant difference exist between groups) when, in fact, the samples are from the same population (no significant difference exist between groups); the null hypothesis is rejected when it is true.

Type II error - Error that occurs when the researcher concludes that no significant difference exists between the samples examined when, in fact, a difference exists; the null hypothesis is regarded as true when it is false.

Hypothesis testing is the sheet anchor of empirical research and in the rapidly emerging practice of evidence-based medicine.The empirical approach to research cannot eliminate uncertainty completely. At the best, it can quantify uncertainty. This uncertainty can be of 2 types: Type I error (falsely rejecting a null hypothesis) and type II error (falsely accepting a null hypothesis). The acceptable magnitudes of type I and type II errors are set in advance and are important for sample size calculations.

• Validity refers to whether in an experiment a tool actually measures what it claims to and whether the results could actually be generalised to the wider world.
• There are two main types of validity - internal and external.
• Internal validity relates to whether changes made to a dependent variable actually change and influence the independent variable – in other words whether the tool measures what it is meant to. If there is high internal validity, you could expect the dependent variable to manipulate the independent variable, whereas if there was low internal validity it would be suspected that confounding variables were playing a part in effecting the independent variable.
• Internal validity can be improved in a few simple ways. In order to ensure an investigating is measuring what it is meant to, investigators can use single and double-blind techniques. A single-blind study is where the participant does not know the condition they are in, and double-blind is where neither the participant nor the experimenter knows what the groups represent. This method ensures there is little demand characteristics, such as trying to behave a certain way because they think that is what is expected of them, and also decreases experimenter effects, as they can not even accidently have a bias to a certain group. Another process that is effective when a repeated measure has been used is counterbalancing and random allocation, as they remove order effects, such as boredom, fatigue.