significance testing
Significance testing is a statistical method used to determine if the results of an experiment or study are likely due to chance or if they reflect a true effect or relationship. Researchers set a threshold, called the significance level (commonly 0.05), to decide whether the observed outcome is statistically significant. If the results fall below this threshold, they conclude that the findings are unlikely to be random, suggesting they are meaningful. This process helps scientists validate their hypotheses and draw reliable conclusions from their data, ensuring that findings are robust and credible.
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Significance testing is a statistical method used to determine if the results of a study or experiment are meaningful or likely due to chance. Researchers set a significance level, commonly 0.05, which means they accept a 5% chance of concluding there is an effect when there isn’t one. By analyzing data, they assess whether observed patterns are strong enough to be considered statistically significant. If results are significant, it suggests that the findings are reliable and worth further consideration; if not, it raises caution about the validity of the findings.