DATA ANALYSIS

Question 1

A/B TEST

Answer 1

TYPE OF INFERENTIAL ANALYSIS

Question 2

DESCRIPTIVE ANALYSIS

Answer 2

Descriptive analysis lets us describe, summarize, and visualize data so that patterns can emerge. Sometimes we’ll only do a descriptive analysis, but most of the time a descriptive analysis is the first step in our analysis process.

Descriptive analyses include measures of central tendency (e.g., mean, median, mode) and spread (e.g., range, quartiles, variance, standard deviation, distribution), which are referred to as descriptives or summary statistics.

Typically, data visualization is also included in descriptive analysis.

Question 3

EXPLORATORY ANALYSIS

Answer 3

Exploratory analyses show us underlying patterns and relationships within datasets.

Exploratory analyses cannot determine causation.

Question 3

INFERENTIAL ANALYSIS

Answer 3

Inferential analysis lets us test a hypothesis on a sample of a population and then extend our conclusions to the whole population.

Question 4

CAUSAL ANALYSIS

Answer 4

CORRELATION =! CAUSATION
Experiments that support causal analysis:

Correlation does not equal causation.
Proving causation is tricky and generally requires very careful experimental design.
Replication, randomization, and control are key components of good experimental design.

Question 5

PREDICTIVE ANALYSIS

Question 6

DATA ANALYSIS

Question 7

SUMMARY STATISTICS

Question 8

CENTRAL TENDENCY

Answer 8

INCLUDED IN DESCRIPTIVE ANALYSIS
e.g., mean, median, mode
EX OF SUMMARY STATISTIC

Question 9

SPREAD

Answer 9

INCLUDED IN DESCRIPTIVE ANALYSIS
(e.g., range, quartiles, variance, standard deviation, distribution
EX OF SUMMARY STATISTIC

Question 10

UNSUPERVISED LEARNING

Question 11

CLUSTERING ALGORITHMS

Question 12

PRINCIPAL COMPONENT ANALYSIS

Question 13

K-MEANS CLUSTERING

Question 14

Rand statistic

Question 15

GOOD EXPERIMENTAL DESIGN

Answer 15

REPLICATION
RANDOMIZATION
CONTROL

Question 16

REPLICATION

Answer 16

GATHER ENOUGH SUBJECTS (REPLICATES) TO SUPPORT STATISTICAL ANALYSIS

Question 17

RANDOMIZATION

Answer 17

ASSIGN SUBJECTS RANDOMLY INTO TREATMENT GROUPS, SO EACH SUBJECT HAS AN EQUAL CHANCE TO BE IN ANY TREATMENT GROUP

Question 18

CONTROL

Answer 18

CONTROL ALL FACTORS THAT ARE NOT THE EXPERIMENT’S FOCUS BUT COULD INFLUENCE THE OUTCOME

Question 19

Causal inference with observational data

Answer 19

requires:

Advanced techniques to identify a causal effect

Meeting very strict conditions

Appropriate statistical tests

Question 20

SUPERVISED MACHINE LEARNING

Answer 20

Supervised machine learning algorithms are trained with labeled data and predict the likelihood of future outcomes.

Supervised machine learning algorithms can only be as good as the data used to train them.

Question 21

POPULAR SUPERVISED MACHINE LEARNING TECHNIQUES

Answer 21

REGRESSION MODELS
SUPPORT VECTOR MACHINES
DEEP LEARNING CCN

Question 22

REGRESSION MODELS

Question 23

SUPPORT VECTOR MACHINES

Question 24

DEEP LEARNING CONVOLUTIONAL NEURAL NETWORKS

Question 25

GARBAGE IN

Answer 25

GARBAGE OUT

Question 26

LOW RISK PREDICTION

Question 27

HIGH RISK PREDICTION

Question 28

AUTOMATION BIAS

Answer 28

Automation bias stems from the idea that computers or machines are more trustworthy than humans because they are more objective. Automation bias is at the root of why people follow their GPS into trouble, even when contradictory information is available.

Question 29

BIAS

Answer 29

SYSTEMATIC ERRORS IN THINKING INFLUENCED BY CULTURAL AND PERSONAL EXPERIENCE.

DISTORT OUR PERCEPTION AND CAUSE US TO MAKE INCORRECT DECISIONS.

Question 30

SELECTION/SAMPLING BIAS

Answer 30

Selection bias occurs when study subjects (i.e., the sample) are not representative of the population. Selection bias can be due to poor study design if the sample is too small or is not randomized. Selection bias can also crop up when the only data available is influenced by historical bias

Question 31

HISTORICAL BIAS

Answer 31

systematic influence based on historic social and cultural beliefs

Question 32

ALGORITHMIC BIAS

Answer 32

Algorithmic bias arises when an algorithm produces systematic and repeatable errors that lead to unfair outcomes, such as privileging one group over another. Algorithmic bias can be initiated through selection bias and then reinforced and perpetuated by other bias types.

Question 33

EVALUATION BIAS

Answer 33

Testing an algorithm with a non-representative dataset leads to evaluation bias. Testing with a non-representative benchmarking dataset would give high overall accuracy scores, even if the algorithms were inaccurate for certain groups.

Question 34

BLACK BOX

Answer 34

the algorithms are proprietary, making them “black boxes”. In addition to not knowing what data were used to train and test the algorithm, we can’t know how it was designed or how it works. As a result, it’s impossible to evaluate the algorithms themselves.

Question 35

CONFIRMATION BIAS

Answer 35

our tendency to seek out information that supports our views. Confirmation bias influences data analysis when we consciously or unconsciously interpret results in a way that supports our original hypothesis. To limit confirmation bias, clearly state hypotheses and goals before starting an analysis, and then honestly evaluate how they influenced our interpretation and reporting of results.

Question 36

OVERGENERALIZATION BIAS

Answer 36

Is inappropriately extending observations made with one dataset to other datasets, leading to overinterpreting results and unjustified extrapolation. To limit overgeneralization bias, be thoughtful when interpreting data, only extend results beyond the dataset used to generate them when it is justified, and only extend results to the proper population.

Question 37

REPORTING BIAS

Answer 37

is the human tendency to only report or share results that affirm our beliefs or hypotheses, also known as “positive” results. Editors, publishers, and readers are also subject to reporting bias as positive results are published, read, and cited more often. To limit reporting bias, report negative results and cite others who do, too.

Question 38

NOMINAL

Question 39

ORDINAL