Confidence Intervals

certainty.py

import math
from scipy.stats import t
import numpy as np

def confidence_interval_for(samples=[], confidence=0.95):
  sample_size = len(samples)
  degrees_freedom = sample_size - 1
  outlier_tails = (1.0 - confidence) / 2.0
  t_distribution_number = -1 * t.ppf(outlier_tails, degrees_freedom)

  step_1 = np.std(samples)/math.sqrt(sample_size)
  step_2 = step_1 * t_distribution_number

  low_end = np.mean(samples) - step_2
  high_end = np.mean(samples) + step_2

  return low_end, high_end