powerdist

README

Hypothesis Testing, Confidence Intervals and Monte Carlo Method

Previous: Distribution of Log-Profits

In this experiment, we establish a methodology for working with sampled data and estimating the precision of the results. Note, that unlike most other experiments, we will be working exclusively with analytical distributions, and therefore, will not need any external data.

• Theory: definitions of hypothesis testing, confidence intervals under hypothesis, Monte Carlo method, and introducing basic statistics
• Normal distributinon: simulating normal distribution with N=250, 5K and 20M samples, evaluating CIs for basic statistics and some general observations
• Student's t-distribution: the same simulation of t-distribution, implications for log-profits
• The Tale of Fat Tails: finding a (alpha) parameter for t-distribution by simulation

Documentation

Overview

Package powerdist is an experiment to study analytical power distributions.

Index

• type PowerDist
  • func (d *PowerDist) AddValue(ctx context.Context, k, v string) error
  • func (d *PowerDist) Prefix(s string) string
  • func (d *PowerDist) Run(ctx context.Context, cfg config.ExperimentConfig) error

Types

type PowerDist

type PowerDist struct {
	// contains filtered or unexported fields
}

PowerDist is an Experiment implementation for studying properties of analytical power and normal distributions.

func (*PowerDist) AddValue

func (d *PowerDist) AddValue(ctx context.Context, k, v string) error

func (*PowerDist) Prefix

func (d *PowerDist) Prefix(s string) string

func (*PowerDist) Run

func (d *PowerDist) Run(ctx context.Context, cfg config.ExperimentConfig) error