statistical tests.
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statistical tests
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statistical tests. <><><><><><>
statistical tests
statistical tests. <><><><><><>
statistical tests
line graphs
Yes, scientists should repeat experiments to compare results, as this helps ensure the reliability and validity of findings. Replication allows researchers to identify any inconsistencies, control for variables, and confirm that results are not due to chance or experimental error. Additionally, repeated experiments can enhance the robustness of scientific claims and contribute to the overall credibility of the research.
A statistical test, such as t-test or ANOVA, is commonly used to compare dependent values in experiments to determine if there is a significant difference between them. These tests provide a statistical measure to determine the likelihood that any differences observed are not due to random chance.
To determine if experimental results are due to chance, researchers commonly use statistical tests such as t-tests, ANOVA (Analysis of Variance), and chi-square tests. These tests evaluate the differences between groups or variables and assess the likelihood that observed differences occurred by random variation. The results are typically interpreted using p-values, where a p-value below a predetermined threshold (commonly 0.05) indicates that the results are statistically significant and unlikely to be due to chance.
compare several treatments and use chance to assign subjects to treatments
replication