ASHIVNI SHEKHAWAT Research scientist at Lyft Inc. |

### Hypothesis Testing

Term | Definition |

H0 | Null hypothesis, the default position that there is no change |

H1 | Alternative hypothesis, the alternative position that there is some change |

Type I error | Rejection of H0, when H0 is true |

Type II error | Failure to reject H0, when H1 is true |

Error | False rejection of H0 |

α, Significance level, false positive rate | Pr(reject H0 | H0 is true); probability of type I error |

β, false negative rate | 1 – Pr(reject H0 | H1 is true); probability of type II error |

Power | Pr(reject H0 | H1 is true); 1 – probability of type II error; 1 – β |

P-value | Probability of observing a result at least as extreme as the observed result, given that H0 and all other model assumptions are true |

Confidence Interval | The set of effect sizes whose test produced P > 0.05 define a 1 – 0.05 = 0.95 or 95 % confidence interval. |

PCE | Per comparison error rate |

PFE | Error rate per family (expected number of false rejections per family) |

FWE | Family wise error rate (probability of at least one error in the family) |

FDR | False discovery rate (expected number of false significances / number of significances) |

### Classification

Term | Definition |

True positive rate; TPR; Recall; Sensitivity | True positives / All positives, i.e. the fraction of positives that are correctly classified by the model |

False positive rate; FPR; Type I error rate | False positives / All negatives, i.e. the fraction of all negatives that are incorrectly classified by the model |

False negative rate; FNR; Type II error rate; Miss rate | False negatives / All positives, i.e. the fraction of all positives that are incorrectly classified by the model |

True negative rate; Selectivity; Specificity | True negatives / All negatives, i.e. the fraction of all negatives that are correctly classified by the model |

Accuracy | True positives + True negatives / Total population, i.e. the fraction of population that is correctly classified by the model |

Precision; PPV; Positive predictive value | True positives / Predicted positives, i.e. the fraction of true positives amongst all predicted positives |

FDR; False discovery rate | False positives / Predicted positives, i.e the fraction of false positives amongst all predicted positives |

Population prevalence | Positives / Population, i.e. the fraction of population that is positive |

F1 score | 2 * Precision * Recall / (Precision + Recall), i.e. the harmonic mean of precision and recall |

### Reinforcement Learning

#### Multi-armed Bandits

Term | Definition |

Action taken by the agent at time step t | |

Estimate of the expected reward from action a at time step t | |

The number of times the action ‘a’ has been played till time step t | |

Reward obtained on the i_th play of action ‘a’ | |

UCB | Upper-Confidence-Bound algorithm for action selection in bandits |

#### Markov Decision Processes

Term | Definition |

MDP | Markov Decision Process |

State, reward, and action at time step t | |

Set of all valid states, rewards, and actions (in state ‘s’) | |

Finite MDP | A MDP in which the set of all states, rewards and actions is finite |

Trajectory | The sequence of state, action, and rewards starting at some given state, |

State dynamics | Also known as the dynamics of a MPD, given by the four argument function . This function gives the probability of reaching a state and receiving a reward s’ by taking an action r in state a. s |

### Limit Theorems

#### Weak law of large numbers

The sample average converges in probability to its expected value.

#### Strong law of large numbers

The sample average converges almost surely to its expected value.

#### Central limit theorem

The average of a large number of IID random variables, when shifted by the population average and scaled by the square root of the number of variables, approaches a normal distribution. More precisely, let {x_{1}, x_{2}, …, x_{n}} be n IID random variables such that E(x_{i}) = μ, Var(x_{i}) = σ^{2}. Let S_{n} = (x_{1} + x_{2} + … + x_{n})/n, then √n(S_{n} – μ) → N(0, σ^{2})

#### Extreme value theorem

The maximum (minimum) of a large number of IID random variables, when centered and scaled properly, converges to a generalized extreme value distribution. The Weibull, Gumbel, and Frechet distributions are members of this family. The centering and scaling here are not as universal as the central limit theorem.

#### Bootstrap theorem (for sample mean)

Let {x_{1}, x_{2}, …, x_{n}} be an i.i.d with sample mean X̅, sample variance S, and a defined population variance. Let X̅_{B} denote the mean of a bootstrap sample drawn from {x_{1}, x_{2}, …, x_{n}}. Then, √n(X̅_{B} – X̅)/S approaches the standard normal distribution in the limit of large n.