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Calculating an appropriate sample size is crucial to minimize sampling error, the discrepancy between sample estimates and actual population parameters. A well-determined sample size ensures that the findings from the sample are statistically reliable and can be generalized to the larger population. Too small a sample can increase sampling error, while an excessively large sample may lead to unnecessary data collection and resource use. Therefore, determining sample size carefully balances accuracy and efficiency in data analysis. Option A is incorrect because larger samples do not always guarantee better results and can be resource-intensive. Option B is incorrect as the goal is to reduce bias, not maximize it. Option D is incorrect because focusing on a single variable limits the scope of analysis. Option E is incorrect as sample size determination does not eliminate the need for data validation, which is necessary to ensure data quality.
√ 27556.11 × √ 624.9 – (22.02) 2 =? × 5.95
1120.04 – 450.18 + 319.98 ÷ 8.06 = ?
24.99 × 32.05 + ? - 27.01 × 19.97 = 29.99 × 27.98
Find the approximate value of Question mark(?). No need to find the exact value.
18.07 × (47.998 ÷ 12.03) + 59.78% of 150.14 – √(255.86) = ...
(124.901) × (11.93) + 219.95 = ? + 114.891 × 13.90
41.5% of ? + 64.69% of 419.1 = 504.2
10.10% of 999.99 + 14.14 × 21.21 - 250.25 = ?
{(1799.89 ÷ 8.18) ÷ 9.09 + 175.15} = 25.05% of ?
(27.08)2 – (14.89)2 – (22.17)2 = ?
159.98% of 4820 + 90.33% of 2840 = ? + 114.99% of 1980