Table 2.
Statistical methods for analyzing NO data in plants.
| Section | Statistical method | Key considerations | References |
|---|---|---|---|
| 3.1 Descriptive statistics for summarizing NO levels | Mean | Represents the average NO level in a dataset. Sensitive to extreme values | (Liu et al., 2016) |
| Median | Middle value in an order dataset. Useful for skewed distributions. | (Huang and Li, 2014) | |
| Standard deviation | Measures variability of NO levels around the mean. Higher SD indicates greater spread. | (Karvonen and Lehtimäki, 2020; Rizwan et al., 2018) | |
| Bar graphs | Used for comparing mean NO levels across different groups | (Kaya et al., 2020) | |
| Box plots | Visualize distribution, median, quartiles, and outliers in NO data | (León et al., 2016; Tang et al., 2019) | |
| 3.2 Inferential statistics for hypothesis testing | t-test (Independent/paired) | Compare NO levels between two groups; independent for unpaired data, paired for repeated data | (Hao et al., 2010; Shu et al., 2025) |
| Analysis of variance | One-way ANOVA for single-factor comparisons; two-way ANOVA for interaction effects | (Paul et al., 2023; Shu et al., 2025) | |
| Regression analysis | Examines relationships between NO levels and predictor variables; linear and non-linear models | (Tahjib-Ul-Arif et al., 2022) | |
| Post-hoc test (Turkey’s HSD, Bonferroni correction) | Identifies specific group differences while controlling for Type I errors | (Agbangba et al., 2024; Ozdemir et al., 2024) | |
| 3.3 Multivariate analysis for complex data sets | Principal component analysis | Reduces dimensionality of NO datasets, identifies dominant patterns | (Nabati et al., 2024) |
| Cluster analysis (k-means clustering) | Groups similar NO data points based on predefined criteria. Useful for phenotypic classification | (Lv et al., 2022) | |
| Structural equation modeling | Models causal relationships among NO levels, environmental factors, and biological responses | (Han et al., 2021; Yu et al., 2021) | |
| 3.4 Time-series analysis for dynamic NO studies | Trend analysis | Identifies long-term patterns and fluctuations in NO levels | (Al Yammahi and Aung, 2023) |
| Autoregressive integrated moving average | Captures temporal dependencies; predicts future NO levels | (Al Yammahi and Aung, 2023) | |
| Wavelet analysis | Detects transient changes and periodicities in NO fluctuations | (Wang et al., 2024) | |
| 3.5 Dealing with confounding factors | Covariate inclusion in regression | Controls for external variables (e.g., light, temperature) influencing NO and outcomes | (León and Costa-Broseta, 2020) |
| Factorial design/Two-way ANOVA | Disentangles interaction effects (e.g., genotype × treatment) | (Paul et al., 2023) |