Write down all the possible sources of variation for an expe

Write down all the possible sources of variation for an experiment to compare the effects of different colours of exam paper on students\' final marks on the exam

Solution

All experimental observations are a combination of signal, the true effect of a variable on an outcome, and noise, the random error inherent in your experimental technique. When designing and analyzing experiments, the goal is to maximize the signal-to-noise ratio so that you can draw accurate conclusions. Six common means of increasing the signal-to-noise ratio are:

Rarely would any one scientific question lend itself to using all six of these signal-to-noise reducing techniques, but to maximize the accuracy of your results, you should try to incorporate as many of these as reasonably possible into your experimental designs. Cost, time, and resource availability will help dictate which techniques are possible and which aren\'t. Table 1, below, outlines the types of scenarios where each technique is most useful.

Quantitative Variables
Technique for increasing the signal-to-noise ratio	What is it?	When is it helpful?	Examples of when to use it
Making repeated measurements	Measuring a single item or event more than once to eliminate error in measuring. More measurements of a single event lead to greater confidence in calculating an accurate average measurement.	Especially helpful if an individual measurement may have a lot of variability; because it has to be made quickly, it is hard to determine the exact endpoint, or is technically difficult and thus prone to errors. Does not add value if the measurement is clear-cut, like the answer to a survey question about a person\'s age or measuring the dimensions of a room in meters.	How many drops of acid does it take to change the color of this indicator solution? Run the reaction several times on aliquots of the same solution. How long does it take for this specific graphics card to heat the air surrounding it to 100°C? Test the same exact graphics card multiple times. How long does this turtle spend underwater before surfacing for a breath? Observe the same turtle multiple times.
Increasing the sample size	Increasing the number of items, or people, that you are collecting data from increases the probability that what you are observing is indicative of the whole population. Calculations can be made to determine how large the sample size needs to be. See the guide on determining the best sample size for a survey for more details.	Especially helpful when you are trying to draw conclusions about an entire population. Does not apply if your conclusions are intended to be specific to an individual or single item.	Do teenagers eat healthy foods? Survey a large number of teens, not just five people who always hang out together, about their daily diets. How do the lung capacities of smokers versus non-smokers compare? Take measurements from many smokers and non-smokers. How long does a 9-volt (V) battery from brand X power a flashlight? Test multiple manufacturing batches of brand X\'s 9-V battery.
Randomization of samples	Using a lottery system to assign samples to different experimental and control groups within a given experiment helps make the starting makeup of the groups as equal as possible, even for variables you might have overlooked. Some experiments can be completely randomized; other involve blockingfirst. Blocking allows for the creation of homogenous groups, like males versus females, and then randomization within the block. This variation is done when the researcher suspects that there may be scientifically important differences between experimental subjects.	Especially critical when the population you are drawing your samples from (which is the population you want to make conclusions about) is very heterogeneous. May not apply if you need to stratify your population because you want to be able to draw different conclusions about each sub-group. For example, men vs. women in a drug study or different types of resistors in a circuit design.	Which fertilization technique increases crop yield the most? Assign fertilizer treatment to each plot of land by lottery, thus evening out effects of other variables, like soil makeup and water content, among the experimental groups. Does this medication decrease osteoporosis? Randomly assign people to determining whether a medication is effective. Randomly assign patients to placebo or medication group.
Randomization of experiments	Using a lottery system to determine the order of carrying out related experiments, rather than relying on an apparently logical order that may introduce other overlooked variables.	Especially critical when you have related experiments from which you are going to draw a single meta conclusion. Applies to both related experiments done serially using the same equipment, and related experiments done in parallel on different equipment.	Does the length of time plastic is pressed in a mold affect the final strength of the plastic? Rather than running experiments testing 10, 20, 30, etc. seconds of pressing back to back, randomize which length of time is tested first, second, etc. The randomization eliminates potential effects from other variables like different amounts of mixing time for the plastic as the experiments progress and changes to the temperature of the mold over the course of all the experiments. Does the color of a mouse maze walls affect the total time it takes for mice to find their way through? For a single experimental repeat, test all the mice on the same day in all the mazes. Mazes should be identical, other than wall color. For each mouse, randomly determine in which order he should be tested in the different-colored mazes. The randomization will eliminate potential contribution of effects like mouse fatigue over the course of the testing.
Repeating experiments	Repeating an experiment more than once helps determine if the data was a fluke, or represents the normal case. It helps guard against jumping to conclusions without enough evidence. The number of repeats depends on many factors, including the spread of the data and the availability of resources. Three repeats is usually a good starting place for evaluating the spread of the data.	Repeating experiments is standard scientific practice for most fields. The exceptions are usually when the scale and cost of the experiments make it impossible. For example, drug trials on a rare medical condition, large-scale sociology experiments, and astronomy observations of rare phenomena.	Which wavelength of visible light emits the most heat? Make repeated measurements for each wavelength, randomize the order you conduct the wavelength experiments in, and repeat the entire set of experiments at least twice more on a different days using, if possible, different equipment. How does caloric restriction affect the lifespan of worms? Start with a statistically large enough sample size, randomly select which worms are calorically restricted and which are allowed to eat as much as they want, repeat the entire experiment at least twice more, starting on different days, with different batches of worms.
Including covariates	Some phenomena are controlled by multiple variables. The outcome is often the sum of these covariates. When modeling these types of phenomena, or analyzing data regarding them, it is best to include all the covariates in the analysis and or model. This gives the most complete picture of what is occurring.	Including covariates is helpful if you are studying a complex phenomenon whose outcome depends on the sum of multiple factors. It is especially necessary if you can\'t tightly control all the variables. For example, when dealing with patients in a drug study, climate data, or analysis of food chains and ecosystems.	How does drug X affect a patient\'s blood pressure? Start with a statistically large enough, randomly selected sample of patients. Give half of them drug X and the other half a placebo. Evaluate the results, taking into account other variables, like gender, age, and weight, known to impact blood pressure. Evaluate how the extinction of a particular species will affect the rest of a local ecosystem. Base predictive models on historical data about the interactions between various species, starting with the one that is endangered and branching out.