I analyzed long-term rainfall data using ITA (Integrated Trend Analysis) in RStudio. The analysis provided me with several parameters including the slope of ITA, standard deviation, correlation, trend indicator, and confidence intervals at 99%, 95%, and 90% levels. While I can observe a trend, I am unsure how to determine the significance of the trend at the 99% (p < 0.01), 95% (p < 0.05), and 90% (p < 0.1) confidence levels.
Kindly requesting to help me?
The simple answer is: You shouldn't use p-values. See here: Article Moving to a World Beyond “p < 0.05”
Most likely, your research question is about quantities (of rainfall) that have real world impact. In such cases, reporting your results should be based on center estimates (e.g. for slope) and the level of certainty as given by the CIs. Use phrases like:
"The center estimate indicates that rainfall increases by 0.1 per year over the observed period, the CIs express high uncertainty, and even a negative trends is still within possibilities."
or:
"The center estimates is practically zero, and given the tight CIs, we can exclude other possibilities with good certainty."
First, you have to formulate your hypothesis. Then assess the p-values for levels 0.01, 0.05, or 0.1 through a linear regression model, which is often used to determine the statistical significance of the observed trend. In your ITA, the p-values could be deployed to evaluate the slope of the trend line, and if the p-value is below a certain significance level (0.01, 0.05, or 0.1), then it means that the trend is statistically significant.
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