Calculate confidence interval for benchmark measurements

2024-06-15 23:00:30 +02:00
parent 19662450b1
commit 2eb620bb33
2 changed files with 3 additions and 2 deletions
@@ -7,6 +7,7 @@ import csv
 import numpy as np
 import time
 import argparse
+from scipy import stats

 class BenchmarkRunner:
    def __init__(self, gps_traces_file_path):
@@ -82,7 +83,7 @@ def calculate_confidence_interval(data):
    assert len(data) == 5, f"Shape: {data.shape}"
    mean = np.mean(data)
    std_err = np.std(data, ddof=1) / np.sqrt(len(data))
-    h = std_err * 1.96 # 95% confidence interval
+    h = std_err * stats.t.ppf((1 + 0.95) / 2., len(data) - 1)  # 95% confidence interval using t-distribution
    return mean, h

 def main():