# Analyzing Statistical Reliability in Forest Inventory and the Impact on Resource Management

August 26, 2023
Henry Harris
Statistical Analysis
Henry Harris, a distinguished biostatistician, holds a Ph.D. from the University of Toronto. With vast experience over 8+ years, he adeptly solves assignments, showcasing mastery in statistical methodologies and transformative research in health sciences.
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Key Topics
• Problem Description:
• Question 1:
• Question 2:
• Question 3:
• Question 4:
• Question 5:

In our exploration we delve into the critical evaluation of forest inventory datasets. We assess whether these datasets meet established statistical benchmarks and explore the implications for effective resource management. Through a series of questions and answers, we determine the dataset's accuracy and precision, offering insights into sample size requirements and the tradeoffs associated with higher statistical confidence. This informative analysis underscores the delicate balance between data precision and practicality, providing valuable knowledge for forest management professionals and researchers.

## Problem Description:

In this Statistical Analysis assignment, we evaluate the statistical reliability of a forest inventory dataset, aiming to determine if it meets the benchmark of ±20% error at a 68% statistical confidence level. Additionally, we explore tradeoffs associated with achieving high statistical confidence and low estimation error in forest inventory.

### Question 1:

Q1:Does the dataset meet the statistical reliability benchmark of +- 20% error at 68% statistical confidence?

Yes, the dataset meets the statistical reliability benchmark. The calculated percent error of 12.6087% is well within the allowable error range of ±20%.

### Question 2:

Q2:What is the percent error of the estimate for the preliminary dataset at this confidence level?

The percent error of the estimate for the preliminary dataset at the 68% confidence level is approximately 12.6087%.

### Question 3:

Q3:How close was the number of plots in your inventory to the minimum required to meet the benchmark?

The number of plots in your inventory was 100 (as mentioned earlier). To meet the benchmark of ±20% error at 68% statistical confidence, you would need a significantly larger dataset of 110,056 plots. This indicates that your inventory had significantly fewer plots than the minimum required to meet the benchmark.

### Question 4:

Q4: How many plots would be needed to generate a basal area estimate at the same allowable error at 95% statistical confidence?

To generate a basal area estimate at the same allowable error of ±20% but at a 95% statistical confidence level, you would require a much larger dataset consisting of 427,499 plots.

### Question 5:

Q5:Are there tradeoffs associated with conducting inventories such that statistical confidence is high/estimation error is low? Explain.

Yes, there are several tradeoffs to consider when aiming for high statistical confidence and low estimation error in forest inventories:

1. Cost and Time: Increasing the number of plots to achieve higher statistical confidence or lower estimation error necessitates more resources, including additional time, labor, and financial investment.
2. Complexity: Managing and analyzing data from a larger number of plots can become more complex, potentially requiring more advanced tools and expertise.
3. Disturbance:Conducting inventories with more plots may lead to increased disturbance in the forest, especially if it involves physical measurements, which can have ecological implications.
4. Precision vs. Practicality: While increasing the number of plots can enhance precision, there is a point of diminishing returns where the added precision may not yield substantial practical benefits.

In summary, while the goal is to achieve high statistical confidence and low estimation error, it is vital to weigh these objectives against the practical considerations and constraints of the inventory process.

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