Why does repeating an experiment increase accuracy

Measurements can be both accurate and precise, accurate but not precise, precise but not accurate, or neither. All measurements are subject to error, which contributes to the uncertainty of the result. Errors can be classified as human error or technical error. Technical error can be broken down into two categories: random error and systematic error. Random error, as the name implies, occur periodically, with no recognizable pattern.

Systematic error occurs when there is a problem with the instrument. For example, a scale could be improperly calibrated and read 0. All measurements would therefore be overestimated by 0. Unless you account for this in your measurement, your measurement will contain some error. The random error will be smaller with a more accurate instrument measurements are made in finer increments and with more repeatability or reproducibility precision. Either of these possibilities, a tendency toward consuming more food or exercising more, might skew the results.

But if the subjects are assigned randomly, such differences are likely to get distributed throughout all the experimental and control groups and thus, not noticeably skew the experimental results. Experiment randomization can be applied in cases where there are a series of tests whose order can be determined via lottery.

In these types of cases, it can be used to reduce unexpected bias in the data. For example, if the goal is to find out what level of sour flavor is tolerable for the average adult, each adult test subject would be given a series of gelatins to taste, each with a different sour intensity. The test subjects would then rate which gelatins they found tolerable and which were too sour to eat.

If the test subjects were all given the gelatins to taste, in increasing order of sour intensity, the result would be an artificially inflated average sour tolerance. Because systematically increasing exposure to the sour flavor temporarily desensitizes the subject's taste buds to the effects of the sourness.

By randomizing the order in which each test subject tastes the various gelatins, the data is less influenced by the bias created by temporary desensitization and the resulting average is more accurate. Repeating an experiment also leads to an increase in the signal-to-noise ratio. Analyzing experimental repeats diminishes the chance that spurious effects like a slightly raised ambient temperature or a machine whose readings are too high are driving the conclusions.

Data from samples are collected together in a single experiment; a repeat of an experiment needs to be independent, meaning as many of the experimental parameters as practically possible should be changed: different samples, different machine, different day, different experimenter etc.

Three repeats of an experiment is generally considered the minimum. Two-thirds may not seem like a lot, but repeats have a diminishing return—more than three and you have to do a lot more repeats to make a major increase in confidence. Even with repeats, there is still a small chance that a single trial will just happen to be closer to the true value than the average.

See Table 2, below, for details. The second reason is that with three repeats, you have a good basis for graphing and using statistical descriptions, like mean and standard error of the mean, to evaluate your data and see if the results are robust enough to make a conclusion from, or if you need to gather more data. In some cases, repeating an experiment is not possible due to resource constraints. For example, a biological survey of a large track of land, like the Amazon rain forest, would only be carried out once.

When repeats are not going to be possible, it is critical to be sure the sample size is sufficiently large. Table 2. Repeating an experiment a few times results in a large increase in the statistical chance that the average of the repeats is more accurate than a single trial of the experiment, but subsequent repeats have diminishing returns.

Table adapted from Gauch, See original text for underlying theory. Many natural systems and scientific phenomena are the sum effect of many factors. These factors, called covariates because they "vary together," collectively control the final outcome.

Although scientists are often interested in assessing how changing a single one of the factors will affect the whole system, it can be impractical, or even impossible, to set up an experiment where just one variable can be changed and evaluated. For example, if you wanted to predict how building a new car-manufacturing plant would affect the local air quality, one way would be to just determine how much air pollution the factory would contribute.

But this model is imprecise. There are other related events that might occur when a new factory is built. For example, the factory would create jobs, and more people might move to the area to take advantage of those jobs.

These people would buy local homes, drive cars, start related industries, and so forth. All these events would also impact local air quality.

So, a more-accurate evaluation would take into account as many of the covariates as possible. Taking covariates into account can also help increase your power of detecting a change.

For example, say you were conducting a study on the ability of a new drug to lower cholesterol. Cholesterol levels are determined by a large number of factors, including: gender, age, family history, diet, physical activity, and weight. In a study with mice, you could control for all of these factors—you could have mice with identical genetics, all of the same age and gender, that are fed the same diet, that weigh the same amount, and perform the same exercise regime.

But it would be impossible to do a similar fully controlled study with humans. And each factor you try to control, the fewer people would be available to your study and the more difficult it would be to recruit subjects. An alternative is to limit only some of the variables, and measure the remaining covariates in order to factor them into your final data-analysis model. Using the model, you can mathematically subtract out the effects of the covariates and still see the effects of the variable in which you're interested: the cholesterol-lowering drug.

These resources provide additional information about how to design experiments and increase the signal-to-noise ratio in scientific data:. Menu Science Projects. Project Guides. View Site Map. Science Projects. Grade Levels. Physical Science. Earth and Environmental Science. Behavioral and Social Science. Now, imagine a measurement that is very precise, but has poor accuracy.

Averaging individual measurements does nothing to improve the accuracy. Accuracy is the degree of closeness to true value. Precision is the degree to which an instrument or process will repeat the same value.

In other words, accuracy is the degree of veracity while precision is the degree of reproducibility. Correct answer: Accuracy deals with how close the measurement got to the accepted measurement.

Precision deals with how consistent the measurement is. Preventing Errors Random error can be reduced by: Using an average measurement from a set of measurements , or.

Increasing sample size. Random errors will shift each measurement from its true value by a random amount and in a random direction. These will affect reliability since they're random but may not affect the overall accuracy of a result.

Random error can be caused by numerous things, such as inconsistencies or imprecision in equipment used to measure data, in experimenter measurements, in individual differences between participants who are being measured, or in experimental procedures.

Reliability is about the consistency of a measure, and validity is about the accuracy of a measure. The extent to which the results really measure what they are supposed to measure. For a typical experiment, you should plan to repeat the experiment at least three times. The more you test the experiment, the more valid your results. It is important for scientists to do repeated trials when doing an experiment because a conclusion must be validated. True because the results of each test should be similar.

Other scientists should be able to repeat your experiment and get similar results. The only way to test a hypothesis is to perform an experiment. A result of an experiment is called an outcome. The sample space of an experiment is the set of all possible outcomes. Three ways to represent a sample space are: to list the possible outcomes, to create a tree diagram, or to create a Venn diagram. If an experiment is replicable it means that anybody can repeat the experiment and get the same results.

It is important that experiments be replicable because if someone else can't repeat what you did, you might have made a mistake. Does repeating an experiment increase accuracy?