line graphs
statistical tests. <><><><><><>
Depending on the experiment, expectation can be very powerful. In a medical experiment, a control group can help determine whether the drug being tested is responsible for the result rather than expectation or something else. For a study of a rare occurrence, a control group may help an experimenter determine whether the outcome is due to chance.
Repeating an experiment enhances the likelihood of obtaining accurate results by reducing the influence of random errors and anomalies. Multiple trials allow researchers to identify consistent patterns and verify findings, increasing the reliability of the data. Additionally, repeated experiments help in establishing a more robust statistical significance, ensuring that observed effects are not due to chance. Overall, repetition fosters greater confidence in the validity of the conclusions drawn from the experiment.
Research
For maintaining reliability internally, a researcher will use as many repeat sample groups as possible, to reduce the chance of an abnormal sample group skewing the results. If you use three replicate samples for each manipulation, and one generates completely different results from the others, then there may be something wrong with the experiment.
statistical tests
statistical tests
statistical tests. <><><><><><>
line graphs
statistical tests. <><><><><><>
statistical tests
replication
replication
statistical tests
statistical tests. <><><><><><>
Statistically the larger the sample size the more significant the results of the experiment are. Chance variation is ruled out.
Repeating an experiment helps to ensure the results are reliable and not just due to chance. Consistent results across multiple trials strengthen the conclusions drawn from the study and increase confidence in the findings.