Ace Statistics Interviews: A Data-driven Approach For Data Scientists

Emma Ding
26 Jun 202313:59
EducationalLearning
32 Likes 10 Comments

TLDRThis video offers a data-driven approach to preparing for common statistics questions in data science job interviews. Host Emma from Amazon.com identifies top concepts like p-value, linear regression, t-tests, correlation coefficient, and types of errors. She explains the p-value's significance in hypothesis testing and provides a real-world example using a productivity app. The video also covers linear regression assumptions, t-test conditions, and the difference between covariance and correlation coefficient. A free cheat sheet is available to help viewers tackle over 40% of potential interview questions, boosting confidence and interview success.

Takeaways
  • πŸ“Š Statistics can be daunting, especially in data science job interviews, where unexpected questions may arise.
  • πŸ’Ό Emma from Amazon.com offers proactive tips and strategies for interviews, including preparing for statistics questions.
  • πŸ” Emma analyzed over 300 statistics interview questions from 50+ companies, identifying common concepts and patterns.
  • πŸ”‘ The most common statistics questions focus on fundamental concepts such as p-value, linear regression, t-test, correlation coefficient, and types of errors.
  • 🎯 P-value is the most important concept, appearing in over 10% of interview questions, and is crucial for hypothesis testing.
  • πŸ“‰ P-value measures the likelihood of observing results as extreme as the sample, assuming the null hypothesis is true, with a common threshold of 0.05.
  • πŸ“ˆ Linear regression assumptions can be remembered by the acronym LINE, standing for Linearity, Independence, Normality, and Equal variance of residuals.
  • 🧐 T-tests are used to determine if two groups have different means and share most assumptions with linear regression, except for the linearity aspect.
  • πŸ”— The correlation coefficient indicates the strength of the linear relationship between two variables, while covariance focuses on the direction of the relationship.
  • 🚫 Type 1 error occurs when incorrectly rejecting a true null hypothesis, while Type 2 error happens when failing to reject a false null hypothesis.
  • πŸ“š Emma provides a free cheat sheet covering frequently asked statistics interview questions to help prepare for over 40% of potential interview questions.
Q & A
  • What is the main purpose of the video?

    -The main purpose of the video is to explore the top statistics questions that often come up in data science job interviews and to present them in an easy-to-understand way, even for those who haven't studied statistics recently.

  • Who is the speaker in the video?

    -The speaker in the video is Emma from Amazon.com, who aims to help viewers land their dream data scientist job by providing tips and strategies for interviews and offer negotiations.

  • How many statistics interview questions did Emma analyze from different companies?

    -Emma analyzed over 300 statistics interview questions from over 50 different companies.

  • What are the top five statistics concepts that frequently come up in interviews according to the video?

    -The top five statistics concepts that frequently come up in interviews are p-value, linear regression, t-test, correlation coefficient, and types of errors.

  • What is the significance of the p-value in data science interviews?

    -The p-value is significant in data science interviews as it is the most commonly asked question, appearing in over 10 percent of the questions, with almost half of the companies asking about it.

  • How is the p-value defined and what does it measure?

    -The p-value is a tool in hypothesis testing that measures the likelihood of obtaining results as extreme as the ones observed in a sample, assuming the null hypothesis is true.

  • What is the common cut-off value for the p-value and what does it imply?

    -The common cut-off value for the p-value is 0.05. If the p-value is less than 0.05, it implies strong evidence against the null hypothesis, allowing for its rejection. If it's greater than 0.05, it indicates weak evidence and the null hypothesis cannot be rejected.

  • Can you provide an example of how to explain the p-value to a non-technical audience?

    -An example given in the video involves a productivity app called Notion. By comparing the productivity of two groups, one using the app and the other not, the p-value can determine if the difference in productivity is statistically significant or due to chance.

  • What are the four key assumptions of linear regression and how can they be remembered?

    -The four key assumptions of linear regression are linearity (L), independence (I), normality (N), and equal variance (E). They can be remembered using the acronym LINE.

  • What does the acronym 'LINE' stand for in the context of linear regression assumptions?

    -The acronym 'LINE' stands for Linearity, Independence, Normality, and Equal variance, which are the four key assumptions to consider in linear regression.

  • How can you differentiate between covariance and correlation coefficient?

    -Covariance focuses on the direction of the relationship between two variables, while the correlation coefficient measures the strength of the linear relationship. The correlation coefficient is unitless and ranges between -1 and 1, whereas covariance has units that are the product of the units of the two variables.

  • What are the two main types of errors in hypothesis testing?

    -The two main types of errors in hypothesis testing are Type I error, which is a false positive (mistakenly concluding there is a difference when there isn't), and Type II error, which is a false negative (failing to detect a true difference).

  • How can you remember the difference between Type I and Type II errors?

    -Type I error can be remembered as a false positive, which contains only one instance of the word 'false'. Type II error can be remembered as a false negative or a 'false false', which helps by repeating the word 'false'.

  • What resource does Emma offer to help viewers prepare for statistics interview questions?

    -Emma offers a free cheat sheet that covers the most frequently asked statistics interview questions, which can be downloaded by clicking the link provided in the video description.

Outlines
00:00
πŸ“Š Mastering Statistics for Data Science Interviews

This paragraph introduces the video's focus on preparing for data science job interviews with a particular emphasis on statistics. The speaker, Emma from Amazon.com, shares her experience and insights gathered from analyzing over 300 interview questions from various companies. The goal is to make complex statistical concepts easy to understand and to highlight the top five most frequently asked questions: p-value, linear regression, t-test, correlation coefficient, and types of errors. The p-value is emphasized as the most important concept, with a structured approach to explaining it in interviews. The video promises to cover all this in under 15 minutes, aiming to boost viewers' confidence in tackling statistical questions in interviews.

05:00
πŸ“ˆ Understanding P-Values and Linear Regression Assumptions

The second paragraph delves into the concept of the p-value, explaining its role in hypothesis testing and how it helps to determine the significance of observed results. A structured approach to explaining p-values in interviews is provided, including its definition, the interpretation of different p-value thresholds, and its practical application in A/B testing. The paragraph also introduces the assumptions of linear regression, using the acronym 'LINE' to remember them: Linear relationship, Independence, Normality, and Equal variance of residuals. A free cheat sheet covering frequently asked statistics interview questions is offered to help viewers prepare more effectively for interviews.

10:01
πŸ” Exploring T-Tests, Correlation, and Hypothesis Testing Errors

This paragraph continues the discussion on statistical concepts important for data science interviews, starting with t-tests. It outlines the assumptions of t-tests using the acronym 'I and E' for Independence and Normality, and Equal variance, and explains the use of t-tests to determine if two groups have different means. The paragraph then contrasts covariance and correlation coefficient, highlighting the correlation coefficient's unitless nature and its range between -1 and 1, versus covariance which is unit-dependent and can vary. Lastly, it addresses the two main types of errors in hypothesis testing: Type I (false positive) and Type II (false negative) errors, providing examples and a mnemonic to help remember the concepts. Additional resources, such as dedicated videos on t-tests and hypothesis testing, are mentioned for further learning.

Mindmap
Type II Error
Type I Error
Application
Difference from Covariance
Use Cases
Assumptions
Importance
Assumptions
Application
Definition
Confidence Building
Encouragement to Learn
Further Learning
Cheat Sheet
Types of Errors
Correlation Coefficient
T-Test
Linear Regression
P-Value
Pattern Recognition
Analysis of Interview Questions
Emma's Introduction
Importance of Statistics
Conclusion
Additional Resources
Top Statistics Concepts
Data-Driven Approach
Introduction
Data Science Interview Preparation
Alert
Keywords
πŸ’‘Statistics
Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. In the context of the video, statistics is the core subject matter, as it is discussed in relation to data science job interviews and the analysis of interview questions.
πŸ’‘Data Science Job Interview
A data science job interview is a professional evaluation process where candidates demonstrate their knowledge and skills in data science. The video focuses on preparing for such interviews, particularly in the area of statistics, to increase the chances of success.
πŸ’‘P-value
The p-value is a statistical measure used in hypothesis testing to determine the probability of obtaining results as extreme as the observed data, assuming the null hypothesis is true. The video emphasizes its importance in data science interviews, noting that it is the most frequently asked concept.
πŸ’‘Hypothesis Testing
Hypothesis testing is a statistical method used to make decisions about a population parameter based on a sample. The p-value is a key component of this process, as discussed in the video, and understanding it is crucial for evaluating the significance of results.
πŸ’‘Linear Regression
Linear regression is a statistical technique for modeling the relationship between a dependent variable and one or more independent variables. The video mentions the assumptions of linear regression, which are essential for ensuring the validity of the model.
πŸ’‘Assumptions
In statistics, assumptions are the conditions that must be met for a statistical method to be valid. The video script discusses the assumptions of linear regression and t-tests, which are fundamental to understanding and applying these statistical tools correctly.
πŸ’‘T-test
A t-test is a statistical test used to compare the means of two groups to determine if there is a significant difference between them. The video outlines the assumptions of t-tests, which are similar to those of linear regression, and their importance in data science interviews.
πŸ’‘Correlation Coefficient
The correlation coefficient is a measure that expresses the extent to which two variables are linearly related. The video differentiates it from covariance, explaining how it quantifies the strength and direction of the relationship between variables.
πŸ’‘Covariance
Covariance is a statistical measure that captures the degree to which two variables vary together. The video script explains how covariance is related to the correlation coefficient but differs in that it is affected by the units of measurement.
πŸ’‘Type I and Type II Errors
Type I and Type II errors are terms used in hypothesis testing to describe incorrect conclusions. A Type I error is a 'false positive' where a difference is concluded when there is none, while a Type II error is a 'false negative' where no difference is found when there actually is one. The video provides examples to illustrate these concepts.
πŸ’‘Confidence
Confidence in the video refers to the self-assurance that interviewees can gain by understanding key statistical concepts. It is mentioned in the context of increasing the chances of success in interviews and boosting one's skills as a data scientist.
Highlights

The video aims to prepare viewers for common statistics questions in data science job interviews.

Emma from Amazon.com provides tips and strategies for job interviews and offer negotiations.

A data-driven approach is used to analyze over 300 statistics interview questions from 50 companies.

Fundamental concepts like p-value, linear regression, t-test, correlation coefficient, and types of errors are frequently asked.

P-value is the most important concept, appearing in over 10% of interview questions.

The p-value measures the likelihood of observing results as extreme as the sample, assuming the null hypothesis is true.

A p-value less than 0.05 indicates strong evidence against the null hypothesis.

The concept of p-value is applied in A/B testing to determine significant differences between groups.

Simple examples, like a productivity app scenario, are used to explain p-value to non-technical audiences.

Linear regression assumptions are remembered using the acronym LINE.

The independence of residuals and normal distribution of residuals are key assumptions for linear regression.

Equal variance assumption ensures consistent spread of residuals across different values of X.

A cheat sheet covering frequently asked statistics interview questions is available for download.

T-tests are used to determine if two groups have different means and have specific assumptions similar to linear regression.

The correlation coefficient measures the strength and direction of the linear relationship between two variables.

Covariance and correlation coefficient are distinguished by their focus on relationship direction and strength.

Type 1 error occurs when concluding a difference where there isn't one, like falsely claiming a change in button color affects conversion rates.

Type 2 error is the failure to detect a real difference, such as not recognizing a button color change's impact on conversion rates.

A mnemonic device is provided to remember type 1 and type 2 errors as 'false positive' and 'false negative'.

Further resources include videos on hypothesis testing and a playlist dedicated to statistics interview questions.

The video encourages continuous learning and curiosity to boost confidence in data science interviews.

Transcripts
00:00

let's be honest statistics can seem

00:02

daunting especially when it comes up

00:04

during a data science job interview

00:05

sometimes it feels like no matter how

00:08

much you have prepared there are those

00:10

unexpected questions that catch you off

00:13

guard so how can you get ready for them

00:15

in this video we are going to explore

00:17

the top statistics questions that often

00:19

come up in these interviews using a

00:22

data-driven approach and we present

00:24

everything in a way that's easy to

00:27

understand even if it's been a while

00:29

since you last studied statistics the

00:31

best part we are going to go over all of

00:33

this in less than 15 minutes are you

00:36

ready let's dive in hey this is

00:37

professionals it's Emma from amazon.com

00:40

we're all about helping you land your

00:42

dream data scientist job we offer

00:44

proactive tips and strategies online

00:46

interviews preparing for interviews and

00:48

negotiating offer so if you're new here

00:51

consider subscribing now let's dive into

00:53

this topic I'll be through my fair share

00:56

of interviews where I stumbled upon

00:58

statistics questions that left me is

01:00

scratching my head let me tell you it

01:02

wasn't a great feeling but as I gained

01:05

more experience I started noticing a

01:07

pattern some Concepts come up more

01:09

frequently than others to back up my

01:12

observations I decided to take a daily

01:15

driven approach I'm gathered and

01:17

analyzed over 300 statistics interview

01:20

questions from over 50 different

01:22

companies and guess what my findings

01:25

confirmed what I suspected I'm really

01:28

excited to share all of this with you

01:29

firstly I discovered that the most

01:32

common asked questions in this

01:33

interviews focus on fundamental concepts

01:36

these concepts are super important to

01:38

grasp so in this video we are going to

01:40

dive into the top five Concepts that

01:43

come up most often they are p-value

01:45

linear regression t-test correlation

01:48

coefficient and types of Errors now out

01:52

of all these Concepts p-value stands out

01:55

as the most important one in data

01:57

science interviews it appears over 10

01:59

percent end of the questions almost half

02:02

of the companies out there asking dates

02:04

to explain what a p-value is so it's

02:07

crucial that you understand this concept

02:09

inside out and how it's commonly used

02:11

this will greatly increase your chances

02:13

of easing your next interview now let's

02:16

take closer look at each of these

02:18

Concepts one by one

02:19

let's dive into the concept of the

02:22

p-value usually you will come across a

02:24

question about how to explain it will

02:27

provide a structured answer by breaking

02:29

it down into a few steps we'll look at

02:31

the definition the meaning of its values

02:34

and its application to answer this

02:36

question the p-value is a useful tool in

02:39

hypothesis testing to help us make sense

02:42

of our observations and draw conclusions

02:44

simply put the p-value measures How

02:47

likely we are to get results as Extreme

02:50

as the ones we observed in our sample

02:52

assuming that our initial assumption the

02:55

null hypothesis is true when we say as

02:58

extreme we mean results that provide

03:01

enough evidence to support an

03:03

alternative hypothesis a low p-value

03:06

means that we have less support of the

03:09

null hypothesis in practice we often use

03:12

a cut value of 0.05 if the p-value is

03:16

less than 0.05 it means we have strong

03:18

evidence against the null hypothesis so

03:21

we can reject it on the other hand if

03:23

the p-value is greater than 0.05 it

03:26

means we have weak evidence against the

03:28

null hypothesis so we can't reject it

03:31

one common application of the p-value is

03:34

in a b testing imagine we have a

03:36

treatment group and a control group and

03:38

we want to determine if there's a

03:39

difference between them in terms of a

03:42

specific metric we run experiment and

03:44

collect data from both groups the

03:47

smaller the p-value the more content we

03:49

can be that there's actually a

03:52

difference between the two groups now

03:54

we've discussed what the p-value is

03:56

another common ask question is how to

03:59

explain it to a non-technical audience a

04:01

helpful method is to use Simple examples

04:04

let's consider a scenario involving a

04:07

productivity app called notion you and

04:10

your colleagues want to improve

04:11

efficiency and accomplish more tasks

04:13

within the limited time you have to test

04:16

effectiveness of notion you decide to

04:19

run an experiment you divide your team

04:21

into two groups one group uses the app

04:24

to manage their tasks and attract their

04:26

progress while the other group continues

04:29

with their typical task management

04:30

methods after a period of time you

04:33

evaluate the productivity of each group

04:35

now we can bring in the concept of the

04:38

p-value to evaluate the significance of

04:40

the results you calculate the p-value to

04:42

determine if the difference in

04:44

productivity between the two groups is

04:47

statistically significant or if it could

04:50

be due to chance a low P value would

04:52

suggest that the app is likely effective

04:55

in boosting productivity on the other

04:58

hand a high P value would indicate that

05:00

the observed differences in productivity

05:02

could be due to random factors other

05:05

than the app itself by considering the

05:07

p-value we can make a more informed

05:10

decision about whether notion is likely

05:12

to be beneficial for improving

05:13

productivity so now that we've covered

05:16

p-value let's dive into another

05:17

important topic linear regression

05:19

another question we often encounter is

05:21

what are the assumptions of linear

05:24

regression don't worry if you don't know

05:26

I'll break them down for you in a simple

05:28

way there are four key assumptions that

05:30

we need to consider and we can remember

05:32

them easily with the acronym line let's

05:35

start with the first assumption

05:36

represented by the letter L it's all

05:39

about the relationship between the

05:40

independent variable let's call it X and

05:42

the dependent variable which we'll call

05:44

Y the sum is that the value on y changes

05:48

in linear manner with X moving on to the

05:51

second assumption represented by the

05:53

letter i it stands for the Assumption of

05:55

statistical Independence of the

05:57

residuals residuals are the differences

06:00

between the actual y values and the

06:02

predicted y values from the regression

06:04

model we want these residuals to be

06:06

independent of each other meaning that

06:09

the value of one residual does not

06:11

influence the value of another

06:13

now let's talk about the third

06:15

assumption represented by the letter N

06:17

it suggests that the residuals follow a

06:20

normal distribution

06:21

in similar terms if we were to plot the

06:24

procedures on graph we'd want them to

06:26

form a nice symmetric bell-shaped curve

06:29

however in large samples this assumption

06:32

becomes less critical

06:33

finally we have the fourth assumption

06:36

represented by the letter e it stands

06:39

for equal variance we want the

06:42

variability of the residuals to be

06:43

consistent across different values of X

06:46

this means that the spread of the

06:48

residuals shouldn't change as X

06:50

increases or decreases so to summarize

06:53

the Assumption of linear regression can

06:55

be remembered using the acronym line we

06:58

have the L for the linear relationship

07:00

between X and Y the I for the

07:02

independence of residuals the N for

07:05

normal distribution of residuals and the

07:07

E for equal variance of residuals and

07:11

that concludes our discussion of the

07:13

first two statistics Concepts in

07:14

intervals now if you are eager to delve

07:17

deeper into this subject and want to

07:19

discover more about the insights I

07:21

gained from analyzing over 300

07:23

statistics interview questions I've got

07:25

something special for you I'll put

07:27

together a handy cheat sheet that covers

07:30

the most frequently Asked statistics

07:32

interview questions

07:33

by familiarizing yourself with these

07:36

questions you'll be equipped to answer

07:37

more than 40 percent of the interview

07:39

questions you may encounter best of all

07:42

you can download this cheat sheet

07:44

absolutely free just click the link

07:46

provided in the video description below

07:48

alright let's now proceed with our list

07:50

of questions next let's dive into the

07:52

topic of t-tests it's a concept that

07:55

often comes up interviews and is really

07:57

useful to understand so what are the

08:00

assumptions of the t-test and one can

08:03

actually use it let's break it down

08:06

aditas is a statistical tool that helps

08:08

us determine if two groups have

08:10

different means and there are a few key

08:12

assumptions we need to keep in mind when

08:14

using it actually most of the

08:16

assumptions of linear regression also

08:18

apply to t-tests except for the one

08:21

about linear relationship so we can

08:23

simplify it using the acronym i and e to

08:25

remember the assumptions let's go

08:27

through them one by one the i in i and e

08:31

stands for Independence it simply means

08:33

that the observations within each group

08:35

or sample should be independent of each

08:38

other in other words the samples in one

08:40

group shouldn't be influenced by other

08:42

samples in the same group moving on to

08:45

the end the i and e which stands for

08:48

normality this assumption tells us that

08:51

the data in each group or the

08:52

differences between the groups should

08:54

roughly follow a normal distribution it

08:57

means that the data should kind of look

08:59

like a bell curve however even if the

09:02

data doesn't perfectly fit that shape

09:04

the T Test can still handle well

09:05

especially when we have a large sample

09:08

size as the central limit theorem states

09:10

that the sampling distribution of the

09:12

sample means will be approximately

09:15

normal regardless of the shape of the

09:17

population distribution

09:19

finally the e in i and e stands for

09:22

equal variance if we are comparing the

09:24

means of two independent groups its

09:27

ideal is the variances of these groups

09:29

are roughly the same this assumption is

09:32

crucial for interpreting the results

09:33

accurately but if the sample balances

09:36

are significantly different we can use

09:38

alternative versions of the t-test like

09:41

a Welch's t-test these variants do not

09:43

require equivalences and can provide

09:46

valid outcomes now if you want to learn

09:48

more about how to use a t-test for one

09:50

sample or two simple tests I've got

09:53

dedicated videos on those topics with

09:55

implementation in Python you can find

09:57

them in this video description below all

09:59

right let's look at another popular

10:01

Topic in statistics the correlation

10:02

coefficient a question that often comes

10:05

up is how to tell the difference between

10:07

covariance and correlation coefficient

10:09

here's a table that summarizes the main

10:12

differences between them the correlation

10:14

coefficient tells us how strong the

10:17

linear relationship between two

10:18

variables is while covariance focuses on

10:21

the direction of the relationship to

10:24

calculate the correlation coefficient

10:25

you divide the covariance by the square

10:28

root of the product of the viruses of

10:31

the two variables now when it comes to

10:33

the units of the measurement the

10:35

correlation coefficient is unitless this

10:38

means that even if you use different

10:40

units for the original variables it

10:42

won't affect the correlation coefficient

10:44

as long as there's a linear relationship

10:46

between the variables on the other hand

10:48

covariance is obtained by multiplying

10:51

the units of the two variables together

10:53

here's another key difference the

10:55

absolute value of the covariance cannot

10:58

be greater than product of the standard

11:00

deviations of the individual variables

11:02

however the correlation coefficient

11:04

always 4 between negative 1 and 1. so

11:08

these two majors help us understand the

11:10

strength and direction of the linear

11:12

relationship between two variables I

11:14

hope this helps explain the distinction

11:16

between them for you now let's move on

11:18

to discussing the fifth question that is

11:20

about different types of errors in

11:22

hypothesis testing there are two main

11:24

types that we need to understand let's

11:26

start with the first one the type 1

11:29

error type 1 error occurs when we

11:31

mistakenly conclude that there is a

11:33

difference between two groups even

11:35

though in reality there isn't to make it

11:38

clearer let's consider an example of a b

11:40

testing imagine we are working for an

11:43

e-commerce company and we want to find

11:45

out if changing the color of the binary

11:47

button on the app will increase the

11:49

number of people who actually make a

11:51

purchase so a Time One error occurs when

11:54

we claim that changing the button color

11:56

from Blue to Yellow will result in a

11:59

significant difference in the conversion

12:01

rate however in reality there's no

12:03

actual difference in conversion rates

12:05

between the two colors now let's move on

12:08

to the second type of error known as a

12:12

type 2 error this happens when we make

12:14

the mistake of failing to reject a false

12:16

null hypothesis in simpler terms it

12:19

means that we conclude there is no

12:22

significant difference between the

12:23

groups when in fact there is a

12:26

difference

12:27

so going back to our previous example we

12:29

might conclude that changing the button

12:31

color won't have a significant impact on

12:33

the conversion rate but the truth is it

12:36

actually does make a difference so we

12:38

fail to detect the difference between

12:40

the two colors

12:41

to help you remember this Concepts

12:43

easily here's a handy tip that I found

12:45

on stack exchange.com you can think of a

12:49

type 2 error as a false negative or a

12:51

false false by repeating the word false

12:54

it becomes easier to remember on the

12:57

other hand a type 1 error is a false

12:59

positive because it only contains one

13:01

instance of the word false

13:03

now if you're interested in diving

13:05

deeper into hypothesis testing I've got

13:07

a video for you check out my video on AV

13:09

testing analysis Made Easy how to use

13:12

hypothesis testing for data science

13:13

intervals in this one we take a closer

13:15

look at how hypothesis testing applies

13:17

to AP testing a topic that is often

13:20

discussed intervals and don't forget to

13:22

also watch my top 5 statistic Concepts

13:25

in data science interviews video it's a

13:27

fantastic resource where you can learn

13:29

about other important Concepts that

13:31

might show up in your next interview by

13:33

the way I've put together a whole

13:35

playlist dedicated to helping you crack

13:37

those tough statistics interview

13:39

questions it's not just beneficial for

13:42

interviews but it will also boost your

13:44

skills as a data scientist remember the

13:47

more you know the more confident you

13:49

will be so stay curious keep learning

13:51

and I have no doubt that you will excel

13:53

in your next interview thanks for

13:55

watching and I will see you soon

13:56

foreign

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