Firstly, the Shapiro-Wilk Test Results for your data:

• Plant 1:Mean: 16.75 Standard Deviation: 6.18 Shapiro-Wilk Test Statistic: 0.696 p-value: 0.0103 (indicating non-normal distribution)
• Plant 2:Mean: 12.00 Standard Deviation: 8.04 Shapiro-Wilk Test Statistic: 0.724 p-value: 0.0213 (indicating non-normal distribution)
• Plant 3:Mean: 13.50 Standard Deviation: 5.57 Shapiro-Wilk Test Statistic: 0.957 p-value: 0.7593 (indicating normal distribution)
• Plant 4:Mean: 13.50 Standard Deviation: 5.45 Shapiro-Wilk Test Statistic: 0.893 p-value: 0.3948 (indicating normal distribution)
• Plant 5:Mean: 10.50 Standard Deviation: 5.00 Shapiro-Wilk Test Statistic: 0.982 p-value: 0.9109 (indicating normal distribution)

Note:

• Plants 3, 4, and 5 do not have any outliers and likely follow a normal distribution based on their p-values (greater than 0.05).
• Plant 1 has an outlier with a value of 26. Plant 2 has an outlier with a value of 24. Therefore, plants 1 and 2 do not follow a normal distribution (p-values less than 0.05).
• Hence, Plants 1 and 2 show deviations from normality,
• so using the IQR method to detect outliers is a more robust choice for these plants.

Chebyshev’s Inequality is too conservative, giving broader ranges where outliers might be expected, thus potentially identifying fewer outliers than the IQR method.

it is less specific and often less practical for detecting outliers in continuous datasets.

Use Chebyshev’s Inequality for distribution-agnostic analysis and IQR for a straightforward approach when the distribution is known or approximately normal. Combining methods can provide a comprehensive view of outliers.