> ## Documentation Index
> Fetch the complete documentation index at: https://ixoworld-mintlify-0aee4309.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Sample Size Calculator
> Calculate statistically robust sample sizes for Emerging Household Energy monitoring workflows.
This page is scoped to **Emerging Household Energy** monitoring operations. It covers sample-size design for surveys and stove-use monitoring evidence quality.
## Quick Start
```bash Calculate Sample theme={null}
curl -X POST https://api.emerging.eco/v1/sampling \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"projectId": "did:ixo:project/789",
"population": 5000,
"confidence": 0.95,
"precision": 0.05,
"expectedMean": 0.8,
"expectedSD": 0.2,
"dropoutRate": 0.15,
"stratification": {
"enabled": true,
"strata": [
{"name": "urban", "weight": 0.6},
{"name": "rural", "weight": 0.4}
]
}
}'
```
```python theme={null}
from emerging import SampleCalculator
calculator = SampleCalculator(
project_id="did:ixo:project/789",
confidence=0.95,
precision=0.05
)
sample_size = calculator.compute(
population=5000,
expected_mean=0.8,
expected_sd=0.2,
dropout_rate=0.15,
stratification={
"enabled": True,
"strata": [
{"name": "urban", "weight": 0.6},
{"name": "rural", "weight": 0.4}
]
}
)
```
## Core Requirements
* **Confidence/Precision**:
* 95% confidence level mandatory
* 5% precision requirement
* Factor imprecision into ER calculations
* **Sample Parameters**:
* Justify expected mean/SD with credible data
* Document ER coverage percentage
* Account for realistic dropout rates
* **Adaptation Requirements**:
* Recalculate after each monitoring period
* Use actual variation from monitoring data
* Adjust size up/down based on real data
* **Independent Strata**:
* Calculate size for each stratum separately
* Maintain confidence/precision per stratum
* Document stratum-specific parameters
* **Stratification Criteria**:
* Based on baseline survey data
* Account for socio-economic factors
* Consider geographical variations
* **Initial Phase**:
* Document selection methodology in MADD
* Ensure verifiable randomness
* Include non-adopting households
* **Verification Phase**:
* Submit project list to FOEN
* Include active and planned projects
* Allow external random selection
## Data Models
### Sample Design
```json theme={null}
{
"designId": "sample-123",
"projectId": "did:ixo:project/789",
"parameters": {
"confidence": 0.95,
"precision": 0.05,
"population": 5000,
"calculatedSize": 357,
"adjustedSize": 420,
"dropoutRate": 0.15,
"erCoverage": 0.85
},
"stratification": {
"method": "proportional",
"strata": [{
"name": "urban",
"population": 3000,
"sampleSize": 252,
"mean": 0.8,
"sd": 0.2
}, {
"name": "rural",
"population": 2000,
"sampleSize": 168,
"mean": 0.75,
"sd": 0.25
}]
},
"verification": {
"status": "approved",
"verifier": "did:ixo:validator/456",
"timestamp": "2024-02-20T10:00:00Z"
}
}
```
## Sample Size Calculator
```python theme={null}
from emerging import CDMCalculator, StratificationEngine
# Initialize CDM calculator
calculator = CDMCalculator()
# Calculate base sample size
base_size = calculator.compute_base_size(
mean=0.8,
sd=0.2,
confidence=0.95,
precision=0.05
)
# Adjust for finite population
adjusted_size = calculator.adjust_for_population(
base_size=base_size,
population=5000
)
# Account for dropout
final_size = calculator.adjust_for_dropout(
sample_size=adjusted_size,
dropout_rate=0.15
)
# Stratify if needed
stratification = StratificationEngine()
strata_sizes = stratification.allocate(
total_size=final_size,
strata=[
{"name": "urban", "weight": 0.6},
{"name": "rural", "weight": 0.4}
]
)
```
## Random Selection
```python theme={null}
from emerging import RandomSelector
selector = RandomSelector(seed=20240220)
# Generate random sample
sample = selector.select(
population_list="household_registry.csv",
sample_size=420,
stratification={
"column": "region",
"sizes": strata_sizes
}
)
# Validate selection
validation = selector.validate_randomness(
selected_sample=sample,
confidence=0.95
)
```
## Monitoring & Adaptation
```python theme={null}
from emerging import SampleMonitor
monitor = SampleMonitor("did:ixo:project/789")
# Analyze current data
stats = monitor.analyze_current_period(
start_date="2024-01-01",
end_date="2024-01-31"
)
# Recalculate sample size
new_size = monitor.recalculate_size(
current_mean=stats.mean,
current_sd=stats.sd
)
# Check if adjustment needed
if monitor.requires_adjustment(new_size):
adjustment = monitor.generate_adjustment_plan()
```
## External Verification
```python theme={null}
from emerging import VerificationSubmission
submission = VerificationSubmission(
project_id="did:ixo:project/789"
)
# Prepare project list
project_list = submission.prepare_project_list(
active_projects=True,
planned_projects=True
)
# Submit for sampling
response = submission.submit_to_foen(
project_list=project_list,
monitoring_period="2024-Q1"
)
```
## Error Handling
Invalid sampling parameters
Insufficient population data
Conflicting stratum definitions
## Best Practices
### Sample Design
* Document all assumptions
* Justify stratification choices
* Consider seasonal variations
* Plan for dropouts
* Maintain stratum independence
### Data Quality
* Validate input parameters
* Monitor dropout rates
* Track response rates
* Document non-responses
* Verify random selection
### Adaptation
* Regular recalculation
* Document changes
* Update stratification
* Maintain precision levels
* Track ER coverage
### Verification
* Prepare FOEN submissions
* Document selection process
* Maintain project lists
* Track verification status
* Archive supporting data
## Next Steps
Apply sampling to surveys
Sensor deployment sampling
Official calculators
FOEN submission process
## Sample Size Adaptation
### Monitoring Period Updates
```python theme={null}
from emerging import AdaptiveSampling
adaptive = AdaptiveSampling(project_id="did:ixo:project/789")
# Analyze current monitoring data
current_stats = adaptive.analyze_period(
start_date="2024-01-01",
end_date="2024-01-31"
)
# Recalculate using actual data
new_sample_size = adaptive.recalculate(
actual_mean=current_stats.mean,
actual_sd=current_stats.sd,
current_dropout_rate=current_stats.dropout_rate
)
# Generate adaptation plan if needed
if adaptive.requires_adjustment(new_sample_size):
plan = adaptive.create_adjustment_plan(
current_size=current_stats.sample_size,
required_size=new_sample_size,
implementation_date="2024-03-01"
)
```
### Dropout Management
```python theme={null}
# Configure with realistic dropout rate
calculator = CDMCalculator(
base_confidence=0.95,
base_precision=0.05,
dropout_buffer=0.15 # 15% dropout rate
)
# Track actual dropouts
dropout_analysis = calculator.analyze_dropouts(
historical_data="2023",
current_period="2024-Q1"
)
# Adjust sample size if needed
adjusted_size = calculator.compensate_for_dropouts(
current_size=400,
actual_dropout_rate=dropout_analysis.rate
)
```
## FOEN Submission Process
```python theme={null}
from emerging import FOENSubmission
submission = FOENSubmission(project_id="did:ixo:project/789")
# Prepare project list for random selection
project_list = submission.prepare_list(
active_projects=True,
planned_projects=True,
monitoring_period="2024-Q2"
)
# Submit for random sampling
response = submission.submit_to_foen(
project_list=project_list,
er_coverage_percentage=0.85, # Document ER coverage
supporting_data={
"mean_justification": "historical_data.pdf",
"sd_calculation": "variance_analysis.pdf"
}
)
```