Answer :
Answer:
Setting a significance level at p<0.05 is common in statistical hypothesis testing because it represents a balance between making confident decisions without being overly conservative. A significance level of p < 0.05 means that there is a 5% chance of observing the results (or more extreme results) given that the null hypothesis is true. In other words, if the observed result falls below this threshold, it is considered statistically significant, indicating that it is unlikely to have occurred by random chance alone. However, setting the significance level at p < 0.0000001 would be excessively stringent. This level is extremely low, indicating an exceedingly high standard for statistical significance. In practical terms, it would mean requiring an extremely strong level of evidence to reject the null hypothesis. Such a stringent threshold would likely lead to many false negatives, where true effects are wrongly deemed non-significant due to the high bar set by the significance level. In summary, while it's important to set a significance level that guards against making decisions based on random chance, setting it too low can lead to overly conservative conclusions and increase the likelihood of type II errors (failing to reject a false null hypothesis). Therefore, a significance level of p<0.05 strikes a reasonable balance between sensitivity and specificity in hypothesis testing.
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