Unit 3- Investigative Biology topic 2- Experimentation c) Experimental design d) Controls

Experimental design- what you need to know Controls, dependent and independent variables. The use and limitations of simple (one independent variable) and multifactorial (more than one independent variable) experimental designs. Advantages and disadvantages in vivo and in vitro studies. Investigators may wish to use groups that already exist, so there is no truly independent variable. These ‘observational’ studies are good at detecting correlation but, as they do not directly test the model, they are less useful for determining causation.

Experimental design- what you need to know Experiments involve the manipulation of the independent variable by the investigator. The experimental treatment group is compared to a control. Simple experiments involve a single independent variable. A multifactorial experiment involves a combination of more than one independent variable or combination of treatments. The control of laboratory conditions allows simple experiments to be conducted more easily than in the field. Similarly, experiments conducted in vivo tend to be more complex than those in vitro. However, a drawback of a simple experiment is that its findings may not be applicable to a wider setting.

Controls- what you need to know Control groups are used for comparison with treatment results. The negative control group provides results in the absence of a treatment. A positive control is a treatment that is included to check that the system can detect a positive result when it occurs.

Experimental design Simple experiments seek to find out how changing one element or variable affects another in order to prove or disprove an idea (the hypothesis). We need to define what the variables are. The independent variable is the factor we seek to change or vary. The dependent variable is the factor that will be affected by changing the independent variable. By varying or changing the independent variable and monitoring its effect on the dependent variable we seek to prove or disprove the hypothesis. To determine the validity of any change in the dependent variable a control is used.

Experimental design will need to take into account: What variables are being investigated? What are the independent and dependent variables? What range of values for the independent variable should be used? How will the dependent variable be monitored/ measured? What is a suitable sample size? What controls are required? What are the confounding variables? How can the confounding variables be controlled/ monitored? How many experimental replicates will be performed?

Simple vs multifactorial experiment Activity- read notes on page 24/25 of scholar and make notes on the difference between simple vs multifactorial experiments.

Laboratory experiments are carried out in an artificial and controlled environment, whereas field experiments are conducted in a natural setting. The control of laboratory conditions allows simple experiments to be conducted more easily the lab than in the field. A draw back of a simple experiment is that its findings may not be applicable to a wider setting.

In vivo vs in vitro studies Experiments conducted in vivo tend to be more complex than those in vitro. In vivoIn vitro

In vivo vs in vitro studies Activity- write notes from the scholar study guide on advantages and disadvantages for in vivo and vitro studies.

Observational studies Investigators may wish to use groups that already exist, so there is no truly independent variable. For example a study investigating the relationship between smoking and lung cancer would recruit an experimental group of people who have been smoking for 20 years and a control group of people who have never smoked.

Observational studies Because observational studies use groups that already exist, the researcher is not able to tightly control the composition of the experimental groups; this means there is the potential for bias in the groups to affect the results of the study and there is no truly independent variable. These ‘observational’ studies are good at detecting correlation but, as they do not directly test the model, they are less useful for determining causation.

Observational studies In the smoking example researchers may suggest that there is a correlation between smoking and increased incidence of lung cancer; however, this does not necessarily suggest causation. Before causation can be determined other factors such as diet, genetics and lifestyle of the subjects would need to be analysed.

Controls A control is a parallel treatment in which the factor being investigated is kept constant or at standard conditions, ie it is not changed. This is compared with the experimental treatment where the independent variable is changed over a range of conditions. The comparison of the control and experimental treatments determines the validity of the experiment. Controls can be either negative or positive.

Controls Negative controls Negative controls anticipate no change in the dependent variable over the course of the treatment. A good example is the addition of denatured enzyme to substrate when studying the effect of that enzyme on the substrate. The methodology is that any change in the dependent variable in the control invalidates the same degree of change in the experimental treatment as it would not be caused by enzyme activity. This does not mean the whole experiment is invalidated, simply that the effect cannot solely be attributed to changing the dependent variable. Such controls are used to negate false positive results.

Controls A negative control in action: Hydrogen peroxide degrades to water and oxygen gas on the application of heat. Catalase is an enzyme that can speed up the breakdown of hydrogen peroxide. To investigate the action of catalase on hydrogen peroxide at different temperatures would seem problematical. The investigation can be done by setting up a control treatment where hydrogen peroxide is exposed to the same temperatures as the experimental treatment but without the presence of the active enzyme. Any effect different to that shown by the control in the experimental treatment must be caused by the enzyme.

Controls Positive controls Positive controls anticipate a predefined change in the dependent variable over the course of the control treatment. A good example is when seeking to test if reagents are active in an assay, eg in an ELISA. The methodology is that no change in the dependent variable in the control invalidates no change in the experimental treatment as it could not be determined that the independent variable caused the effect. This would mean the whole experiment is invalidated because the effect cannot be attributed to changing the dependent variable. Such controls are used to negate false negative results.

Experimental design- what you need to know Controls, dependent and independent variables. The use and limitations of simple (one independent variable) and multifactorial (more than one independent variable) experimental designs. Advantages and disadvantages in vivo and in vitro studies. Investigators may wish to use groups that already exist, so there is no truly independent variable. These ‘observational’ studies are good at detecting correlation but, as they do not directly test the model, they are less useful for determining causation.

Experimental design- what you need to know Experiments involve the manipulation of the independent variable by the investigator. The experimental treatment group is compared to a control. Simple experiments involve a single independent variable. A multifactorial experiment involves a combination of more than one independent variable or combination of treatments. The control of laboratory conditions allows simple experiments to be conducted more easily than in the field. Similarly, experiments conducted in vivo tend to be more complex than those in vitro. However, a drawback of a simple experiment is that its findings may not be applicable to a wider setting.

Controls- what you need to know Control groups are used for comparison with treatment results. The negative control group provides results in the absence of a treatment. A positive control is a treatment that is included to check that the system can detect a positive result when it occurs.