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The p-value quantifies how likely it is to observe the sample data (or more extreme results) under the assumption that the null hypothesis is correct. A low p-value (e.g., <0.05) suggests that the observed data is unlikely under the null hypothesis, leading to its rejection in favor of the alternative hypothesis. For example, if testing a new drug, a p-value of 0.03 indicates a 3% chance that the observed effect is due to random variation under the null hypothesis. Why Other Options Are Wrong : A) The p-value doesn’t measure the truth of the null hypothesis itself. C) This describes the power of a test, not the p-value. D) The threshold (e.g., 0.05) is set by the researcher; it’s not the p-value. E) This describes R-squared in regression analysis, not hypothesis testing.
1(1/2)+ 11(1/3) + 111(1/2) + 1111(1/3) + 11111(1/2) = ?
Find the simplified value of the following expression:
[{12 + (13 × 4 ÷ 2 ÷ 2) × 5 – 8} + 13 of 8]
3/4 of 2000 + √1024 = ? + 12.5% of 3200
32% of 450 + 60% of 150 = ? × 9
√4096 + 4/5 of 780 − ? = 296
9 × 40× 242 × 182= ?2
33 × 5 - ?% of 250 = 62 - 6
√ (573 – 819 + 775) = ? ÷ 3
If a nine-digit number 389x6378y is divisible by 72, then the value of √(6x + 7y) will be∶