@Test(expected = IllegalArgumentException.class) public void scaledZInputs() throws Exception { DecisionAdjuster.scaledZ(0.95, 0); }
public static Double scaledZ(final double confidence_level, final double max_decisions) { if (confidence_level <= 0.0 || confidence_level >= 1.0) { throw new IllegalArgumentException("Confidence level must be in the interval (0, 1)."); } if (max_decisions <= 0.0) { throw new IllegalArgumentException("Scale factor must be positive."); } double significance_level = 1.0 - confidence_level; return normalInverse(1.0 - significance_level / max_decisions / 2.); }
DecisionAdjuster { public static Double scaledZ(final double confidence_level, final double max_decisions) { if (confidence_level <= 0.0 || confidence_level >= 1.0) { throw new IllegalArgumentException("Confidence level must be in the interval (0, 1)."); } if (max_decisions <= 0.0) { throw new IllegalArgumentException("Scale factor must be positive."); } double significance_level = 1.0 - confidence_level; return normalInverse(1.0 - significance_level / max_decisions / 2.); } }
DecisionAdjuster { public static Double scaledZ(final double confidence_level, final double max_decisions) { if (confidence_level <= 0.0 || confidence_level >= 1.0) { throw new IllegalArgumentException("Confidence level must be in the interval (0, 1)."); } if (max_decisions <= 0.0) { throw new IllegalArgumentException("Scale factor must be positive."); } double significance_level = 1.0 - confidence_level; return normalInverse(1.0 - significance_level / max_decisions / 2.); } private  DecisionAdjuster(); }
DecisionAdjuster { public static Double scaledZ(final double confidence_level, final double max_decisions) { if (confidence_level <= 0.0 || confidence_level >= 1.0) { throw new IllegalArgumentException("Confidence level must be in the interval (0, 1)."); } if (max_decisions <= 0.0) { throw new IllegalArgumentException("Scale factor must be positive."); } double significance_level = 1.0 - confidence_level; return normalInverse(1.0 - significance_level / max_decisions / 2.); } private  DecisionAdjuster(); static Double scaledZ(final double confidence_level, final double max_decisions); static Double scaledZ(final double confidence_level); static Double calcMaxDecisions(final long min_impressions, final long max_impressions); }
DecisionAdjuster { public static Double scaledZ(final double confidence_level, final double max_decisions) { if (confidence_level <= 0.0 || confidence_level >= 1.0) { throw new IllegalArgumentException("Confidence level must be in the interval (0, 1)."); } if (max_decisions <= 0.0) { throw new IllegalArgumentException("Scale factor must be positive."); } double significance_level = 1.0 - confidence_level; return normalInverse(1.0 - significance_level / max_decisions / 2.); } private  DecisionAdjuster(); static Double scaledZ(final double confidence_level, final double max_decisions); static Double scaledZ(final double confidence_level); static Double calcMaxDecisions(final long min_impressions, final long max_impressions); static final long DEFAULT_MIN_IMPRESSIONS; static final long DEFAULT_MAX_IMPRESSIONS; static final double DEFAULT_MAX_DECISIONS; }