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Why is replication important in experimental?
Observed results are less likely to be affected by random chance.
Why is replication in experimental design?
Observed results are less likely to be affected by random chance.
Why is replication important and experimental design?
Observed results are less likely to be affected by random chance.
What is experimental variation?
Experimental variation refers to the differences in outcomes or results that are observed between different trials or groups within an experiment. These variations can arise due to factors such as measurement errors, environmental conditions, or random chance. Minimizing experimental variation is important to ensure the reliability and validity of the experimental results.
What is this called when you experiment more than once to reduce chance of errors this is called?
When you experiment more than once to reduce the chance of errors, this is called "replication." Replication helps ensure that results are reliable and not due to random chance or experimental error. It enhances the validity of the findings by confirming that similar outcomes are consistently observed under the same conditions.
Scientists do their experimental tests more than once so they can reduce the effects of chance errors.?
Yes? There is always a chance that experimental results happened by chance (something called a Type I error in Statistics which is bad, but over-emphasized). Replications (which are not done often enough) help protect us against such "accidental" effects because reproducing the results by chance is FAR less likely than just getting them once by chance. But reproducing REAL effects should be quite easy. Though if it is the same scientist, in the same lab, it is possible the results can be replicated even when they shouldn't be replicated, not by chance, but because of something systematic (dirty or faulty equipment, poor randomization, experimenter accidently communicates something to the participant, ...).
What is the name of getting the same results when an experiment is done many times?
The phenomenon of obtaining the same results when an experiment is repeated multiple times is known as "reliability." Reliable results indicate that the experimental methods and measurements are consistent, allowing for confidence in the findings. This consistency is crucial for validating scientific conclusions and ensuring that the results are not due to random chance or experimental error.
What is called when scientists do their experimental tests more than once so they can reduce the effects of chance errors?
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When you do an experiment multiple times to verify your data what is it called?
When you perform an experiment multiple times to verify your data, it is called replication. Replication helps ensure the reliability and validity of the results, as consistent outcomes across repeated trials indicate that the findings are robust. This process is essential in scientific research to confirm that the results are not due to chance or experimental error.
What is it called when Every participant in an experiment has an equal chance of receiving one of the treatments?
When every participant in an experiment has an equal chance of receiving one of the treatments, it is referred to as random assignment. This method helps to eliminate bias and ensures that the groups are comparable, allowing for more reliable and valid results in experimental research.
When scientisits do their experimental tests more than once so they can reduce the effects of chance errors this is called conducting?
experiment
Are expected results due to chance or are they arrived at mathematically?
Expected results can arise from both chance and mathematical calculations. In probabilistic contexts, expected values are calculated using mathematical formulas based on probabilities and outcomes. However, in experimental settings, observed results may also reflect random variations or chance. Therefore, while mathematical methods provide a framework for predicting expected results, actual outcomes can be influenced by stochastic factors.
