This is a fantastic overview of statistics! I feel like I'm listening to a great story, and cant wait to hear all the other chapters. As a college student enrolled in stats, I am so glad I stumbled across this video! Your narration manages to take the painfully mundane content of statistics and turn it into riveting story. Thanks to you, the probability of me passing the class just skyrocketed. :)
You got to be kidding right? This only makes sense if you follow sports at all which I do not. Also, I do not understand the term, “discrete“ unless you explain it to me. Which you didn’t. Not trying to insult a video, just trying to point out that understanding statistics or any mathematical concept I think it depends a lot on one’s frame of reference. For those of us who hear nothing but static the moment conversation turns to sports, I’m thinking there has to be a more generalized frame of reference that more than sports fans understand. And on that very point, how is a player’s position on a team “ordinal” - wait, I’m already asleep. 😴Finally, it would be helpful to understand the overall aim of statistics first rather than diving into the terms immediately. I don’t care about terms unless they lead me to a next stop in the overall direction of where I’m going, and I never get into a car when I don’t have a general sense of where I’m actually going. Because why would I do anything where I don’t have an overall sense to the point of it, actually? My 2 cents, because I do find statistics to be fascinating, even if I’m only minimally good at them, and I was so hoping to like this video.
I spent an entire semester crying my way through trying to understand Statistics. Literally sobbing as I was studying. 4 years later and I'm retaking the course (someplace new) and found this video. Suddenly all of the previous class makes sense and I can see what the Prof was trying to do... he just couldn't present it in a way that made any sense. Always in the weeds of details without an overarching idea of what I was looking at. Thank you so so much!
Yep, too many stats courses, teachers and books, focus too heavily on the math ( and the jargon of assumptions) without first establishing the general principles of what stats are about. Therefore some students get put off by finding the math difficult. Add to this the usual time limits set by most courses etc, and it's no wonder why many ( a high stats %) get put off from learning stats. Science is also often taught in ways that put people off. First and foremost the principles of Science ( the philosophy regarding why we use the Scientific methods) should be learnt, rather than just try to cram peoples "heads" full of Science based facts. Once the value of those Scientific methods are established, people will more probably value Science and Stats ( rather than, for example, incorrectly believe that opinions are comparable to empirical evidence).
0:00 Introduction - 30 minute summary of statistics 1:18 Data Types - numerical (continuous and discrete) or categorical (nominal and ordinal) Steve Curry example, nominal data. Is a proportion considered discrete or continuous numerical variables? 8:04 Distributions - Probability density function - P.D.F. Normal (bell curve), uniform (bi-modal), left and right skew -shapes. PDF is unique to an event and based on data. Probability of choosing data (a player) of a certain attributes (conditions on dimensional attributes - a certain height). Sampling , averaging many events, can be viewed as having a steeping PDF and a lower variance. 13:51 Sampling and Estimation - more data provides better confident interval. Sampling mean - average , must be numeric (mu) Makes sense that none numerical data uses mode and median x (with bar hat) standard deviation (row) - difference from the average, on average s proportion (pi) - is a measure of data, whether nominal or numerical. p correlation (row) - the connection between dimensions r gradient (beta) - change between dimensions. Regression b (theta) - general variable. (and the greek with statistic and english for probability of sampling) 22:30 Hypothesis testing - binomial distribution (PDF) Since 50% is null, 7.5 is the likely outcome, middle of the bell. less than 5% would reject the null. In this case, we'd not reject. We don't accept or prove the alternative, we reject or don't reject the null. Legal system, innocent until proven guilty. 31:28 p-values - our test statistic is 30.4% (area under 9). 35:35 BONUS SECTION: p-hacking I find it interesting that all data is conceptual, or also referred to as nominal (I don’t mean or in the set theory this OR that categorization way, just as a different name for the same concept way). That includes data that is numerical. Just because we assign a character "3," doesn't make 3 not a name. Numerical is a name that can be used as a measure. Measures are about numerical comparison. Both the concept of numerical and measure are tied to counting, while nominal is not. When we say nominal data, we literally mean, a name of a concept, but we also mean "not a measure” or “Lacking ordering of elements.” (Ordering is always a numerical property of “distance” but distance is not always a property of ordering, as nominal data can be given structure and ordinality.) The measure (or what you are calling a proportion of data - where we use numerical values as a comparative evaluation) of a dimension of all or any data (to my understanding - correct me if you disagree with my narrative, I'm only starting to learn about data) can have the attribute (a specific entry of a dimension) of "numerical" (As opposed to "nominal," but if we are being honest, "non-numerical" is more descriptive). I'd argue that reality is continuous. Discrete is the way that we process or model reality. The same way that we process continuous functions that represent physical signals in reality (currents, voltages, energy, ...). We discretize to understand and find meaning. The ship of Theseus is a good example of a continuous reality. I'd even go as far as to say you can't process data without discretization. Calculus and differential equations attempt to approach this continuous reality, but show that the reality is right out of our reach for understanding as we discretize our understanding. Continuity is a form or experience and reality, and, as such can't be presented unless in a discrete way. Other narratives around data types is: 1. Nominal, 2. Ordinal, 3. Interval/Ratio. Both measures (proportions), produced from the given data set, are example discrete dimensions of data. I'd argue that counting is not continuous. Saying numerical data has an attribute of "continuous," isn't illogical. If it is, I'd challenge you to provide an example. To say that data has an attribute of nominal (non-numeric) or numerical, is logical. This topic deserves a whole essay to do it justice. It is worth noting that there is value to comparing the concept of continuous (changes don't occur instantaneously) and discrete, but I suspect we are a little too liberal in our use of saying numerical data is either continuous or discrete. I don't think it is an accurate statement, but it is part of how we communicate meaning. We should change that. Another observations are that the numerical is also ordinal data and that ordinal is a subset of nominal and numerical is a subset of ordinal. The key with math and legal and politics and life, is understanding what and how you should look at something. That is the toughest part about math. Identifying what you are looking at and how you should conceptualize it. Example. It is a function, an equation, and/or a relationship. I suspect it is much the same with Stats and Prob. Great Philosophical video. I'll have to check out some of your others. www.zstatistics.com/podcast
Chris Cf ockrell Chris Cockrell , Just a word of caution... if you are quite sure of yourself, write a lot. If not, consider not writing so much. The possibility of a mistake grows almost surely as a function of your sureness, but little helps you if you make one.
@@BruceNitroxpro So think little, write very little, so you make no mistakes? So you aren't wrong? I'd rather risk being wrong. If you disagree with my thought, explain. I could be totally wrong. I take no issue with that. Education is too often about regurgitation, not understanding. It often lacks thought and consideration.
Chris Cockrell , Not sure you are even replying to one of my comments. I can't find it if you are... If you are just posting an opinion, go for it. I agree with the part I see.
hi, i did math stats, anova and regression end of last year, this year i did stochastic and im currently doing time series. I want to deepen my stats knowlege any sugestions on which courses i should do next? could you give me a map on where to go next?
I love the calming voice and the lack of a music track in the background, it makes the video so much easier to listen to. So many of these videos are nearly impossible to listen to due to invasive music.
...and don't forget the ugly noices of some specialists and the fast running speeches of them - so you just can't follow, get only fragments and bad feelings.
I became obsessed with NBA statistics when I was 12 after buying my first basketball cards. There was a problem, some cards didn't include averages, only totals. There were cards that included less seasons, but included career totals, etc. So I to applied the basic statistics I knew to get the missing info (91 pre-internet living in Mexico btw) and I got most of the concepts from doing that, but without the terminology of course. So now I'm trying to learn more so I can fill in the gaps, mainly because I have an interest in risk theory, probability and all things related. Your videos are great and easy to understand, thank you so much!
I'm a recently retired British (high-) secondary school teacher of mathematics; your approach appeals to me very much, and is similar to way I have taught the subject. I find your explanations really engaging, relevant and crstal clear! Well done, and thank you for helping to spread a better understanding of mathematical ideas and techniques.
Zed, you are an amazing teacher/ professor/ trainer, I must say. You took even challenge so humbly, yet you didn't miss your purpose, i.e., to teach us :) Kudos to you! Im happy I found a guru like you! Now , to answer your 1st question in data for proportion, I think it's continuous data.
This is a very helpful video for someone who has been out of school for over 10 years. I am currently pursuing MSN and MBA. This audiovisual presentation/video suits my learning style as I am not a fan ever of reading thick textbooks. Maybe articles and short paragraphs I can read but majority, I like watching videos, looking at diagrams and pictures. I am an auditory, visual and kinesthetic learner. I write while I listen to the narration, and this helps me a lot! Thank you so much.
do more of these, I love watching speedrun explanation videos the day before the exam, feels good and you reabsorb so many things just when you hear it from a different source again! friendly greetings from Germany!
Thank you for this. I'm back in school after nearly 20 years, and I honestly don't know how my young brain was so much better able to understand these concepts. I find that I need a lot of supplementary support now, and this video has been very useful.
As someone who needed a review os statistics and wanted to know the concepts and not the fórmulas (My teachers used to just throw formulas) I GREATLY apreciate this video. I am not even that much in to statistics but have my sub man!
Even though this video is just an inch longer than 30 mins, it is still a huge, gigantic feat - you have done the impossible once again, Justin. This is a Guiness World Record-worthy effort. Cheers to you 🍻
Thank you. I'm a BA in psychology, but it's been a while and now getting ready for an MA entrance exam this is a really good intro before all the equations come into the picture.
Gee I wish I'd found this video several weeks ago when I first started college stats class online. thankfully I have an amazing instructor who understands my frustrations and has helped me sooo much in this class. But I wish I'd found this back 7.5 wks ago when or even before i started this class. this makes so much sense now! Thank you!
I am on the verge of making a change in my job role that involves learning statistics, among a whole bunch of other things, after a decade-long career in ITSM. I was uncertain as to whether I would enjoy spending time going deeper into statistics again. This video is what I needed to get started. Thank you for sharing your knowledge. I have subscribed to your channel, and looking forward to seeing more content in the future. 🙏
p-hacking is an interesting way to figure out things that are actually worth going out and doing MORE research on. that's kind of what is neat about data aggregation... there may be emergent trends or effects that can indicate real problems in need of more exploration. For example, in my home region they found that certain area codes were connected to higher cancer rates. This doesn't mean that the area code itself caused cancer, but that there was some phenomenon that resulted in increased rates of cancer. More research revealed it was two key factors: higher rates of persons with sedentary jobs (truck drivers, etc), and higher rates of smoking in a cultural group that was more dense in specific areas. These discoveries made it possible to design meaningful health interventions that could specifically target those groups in that particular sample.
I was apprehensive ( 2nd time taking class) to start this class in the Fall which I need to take to graduate. After listening to you explain in great detail, I'm not feeling apprehensive anymore. I thank you for this video.
I always felt bad for this channel having very few viewers compared to its content. I am so happy to see that such a video has 1M views. I hope it gets 10M+ views.
Great video. Mathematically speaking, Steph's shooting percentage is a ratio and not a proportion, it's the ratio of 3-pointers made divided by total attempts. A proportion is when two ratios are equated, for example, how many 3-pointers is Steph expected to make if he continues to shoot say another 250 3-pointers. Also, in the scientific method, an effect is hypothesized, then experiments are conducted, and if the hypothesis appears to be valid, then the hypothesis is advanced to a theory.
It is funny that statistics teachers tend to use baseball or some other sports examples rather frequently when only a small percentage of learners can even understand those sports enough to make sense of the statistics that is being taught based upon those examples..
A "small percentage" of learners can understand sports? What bad statistics are you looking at? Or are you simply falling victim to a laughably revealing case of Observer Bias? Brother, do yourself a favor and pick up a sport to watch, you'll find it makes it easier for normal people to like you.
I dont relate to any sport either, so these examples are difficult for me too. And sports isn't the only thing that gets you "out in the real world". Maybe examples of everyday chores or student life related examples would make more sense. @partymcfly5549 Take a look at your comment... It itself is Observer Bias... Quite laughable
I have a psychology test tomorrow - cannot get to grips with the damn stats even though we have covered it many times in my degree! Thanks so much for your explanation! And now after knowing nothing about NBA I can talk to my NBA fan friend about Steph Curry! Sweeeeet :)
Thanks for taking this challenge and sharing with us such a useful video! :-) Can you please also consider making a video with the *mathematical fundamentals of statistics* that would create a good *foundation* for us to be able to *solve any statistics problem?* I will be taking a *college statistics class* but I'm nervous because I've never been good at math. I really wish I could see a well-structured video that teaches me *the core basics* that will help me *perform the mathematics of statistics* and do well in a college statistics class. It can be a half-an-hour class but it can also be one hour or more, as long as the fundamentals are thoroughly covered in a *structured* way. I would still consider it *ambitious* even if it's a few hours long video because it's still much shorter than a college statistics math class, which tends to be 3-4 hours per week, and it lasts for many weeks. I want to know *the math of it* but I also want to see it explained with actual *real world examples.* I have to learn how to do the math in order to do well in the class but examples and visuals help a lot too for remembering things. A combination of both would be best, although the math part is somewhat more important because it can be a lot harder to learn it. I really hope you consider making a video like this because it would help a lot. It would help make my semester feel much less tortuous, if I can prepare ahead of time. Thank you so much for your time! :D
Statistics grad student here. Great video. Obviously there are a lot of details and examples worth covering also if a person really wants to learn the material well, but this is a great overview/review. Learning statistics will definitely change the way you think and function in the world.
Exactly i have been a while thinking how statistics in my country is so neglected in school education although we make as humans a lot of interpretations based on statistics and counting. As a result we fall in a lot of fallacies. Also although we have a lot of data which should help us unconsiously think using law of large numbers but will fail because we care about our emotions and desire driven more than thinking logicalll.
Very well instructed Sir! I am going back to school to further my career in business.This video helps a ton with understanding the basic concepts and as you said "building your intuition around statistics" I will send links to all my friends. You rock and just gained a Subscriber. 🍻
Trying to wrap my head around the p-hacking subject (36:13): Study 1: So if you conduct a research project where you only look at a single effect, you wouldn't expect your calculated p-value to be less than 0.05 if the null hypothesis is true, because that would happen in only 1 out of every 20th time you repeat your study. Thus, if I observe p < 0.05 for my single effect, the odds are in favour of this being a true signal that the null hypothesis is wrong. However, if I (or any of my peers) repeat my project 19 more times, I would actually expect one of the tests to result in p < 0.05 even if the nullhypothesis is true, just because that's the actual definition of the p-value (given a significance level of 0.05). Study 2: An alternative way research is conducted is to collect a lot of data, and then test for several effects. Every single effect you test will be the equivalent of conducting study 1. So, by testing 20 different effects in your data set you would actually expect one of your effects to have p < 0.05 even though the null hypothesis for that effect is true (aka. falsely concluding the null hypothesis is wrong). Is this correct?
Thank you, thank you for easing the pressure in my brain that is new to statistics. I have read a few chapters, but your video crystalizes basic statistics for me.
Is a proportion a discrete or continuous numerical data? Proportion is a percentage or part of a whole, right? So two thoughts... 1. Percentages seem to be continuous, because you can have almost any number along the line from 1 to 100 %. For example, half of the cookies in the cookie jar could be gone, but there are crumbs so these might count towards the percentage if the way we are measuring is by weight. (I know more about cookies more than basketball obviously). 2. But then it also seems that it is discrete because it can only be some part of the whole, like 1/10th is always .1 or 10% of the whole (10). Or in the case of the cookie jar, there is still 51.25 % (with the crumbs), but this is a discrete proportion of the whole? Where is my thinking off here? (I wish I had a stronger intuition for numbers and I’m trying to built it, but this whole categorizing data seems slippery to me still).
After learning most of these concepts separately, it's so satisfying to watch how you went over them in such a simplified and comprehensive way. Thank you so much!
I am a medical student who wanted to get the hang of this, for my research projects and I can safely say with a p value of 0.05 that I clearly understood these wild concepts.Thanks to my guy!^^
Great video but choice of sports was wrong , maybe Football or Lawn Tennis which is more universal could have been a better sports. Had no clue what "3 pint percentage " is in Basketball
I was watching this video to brush up on statistics concepts I have studied before. It was an amazing refresher and also gave me new perspectives to look at certain concepts and see them in a simpler and more natural light. Thank you so much!
Hi E.D.! Steph Curry is considered to be the best 3 point shooter of all time. His all time percentage is 43.5%. So no, 50% is probably a little optimistic for most mortals. Though I swear I used to bomb threes in my backyard closer to 70%.
@@zedstatistics There is a huge difference between shooting during practice and shooting during the game. It would have been a great analogy for why understanding research is important. A lab test needs to know what it is testing before it can test it.
This is mind blowing video for the beginners. I loved watching it. You have made statistics so simple to understand. I have started loving this subject because of you made it so easy to understand. Thank you and May God bless you Justin n Team.
This just helped me immensely! Soo sooooo grateful for your video! Thank you very much! And I wholeheartedly agree with Jennifer's comment that's pinned. I feel like I'm listening to a story, rather than learning theoretical jargon. I understand the subject so much better now!
I have started learning Data Analysis. And was looking for something short to get into the game of statistics faster, this video was really motivating. Thank you very much for your effort. I really admire your effort for making this so simple.
The most important massage is on the end about p hacking. Very well explained. I kind of knew what it is about but could never explain it to others. Thanks!
Statistic is how to know things happened in any population without doing study on all members of population because of lack of sources. So, you have to design method to study sample of population but your result should represent population.
you're a brillianttt !! I'm a pro card counter and we use statistics a lot. I've never loved maths or statistics in my life, I just thought it was cool when I was younger age but wasn't my cup of tea. But now I was interested and started to research and bumped into your video which made it even more interesting!!!! Thank you so much for your hard work and very simple explanations on all of that!!!!
I got a problem for you that I'd love to hear the answer too. You see a scorpion on the arm of a wicker couch crawl into the arm of the chair. A day goes by. The area is an enclosed rectangle around 7ft by 24 feet. 8ft ceiling. With a small cat door. There's a few tables. Some cabinets, the love seat that the scorpian crawled into, a wooden reclining chair, and an office chair. What's the percentage that the scorpian is still in the love seat? Also, which chair would be the best to sit in and not get got by a pokie grabbing thing of pain?
You have become a part of my study material for an exam in my Data Science class, we did not review much of statistics, only that we are expected to know it already. Many thanks for your contribution!
Because my professor was struggling from some personal matters, we only got halfway through the syllabus. I'm glad that there's still videos online like this one where I can still learn those important skills
@7:50 Is Proportion a discrete or continuous numerical data? I believe it is a discrete numerical data that asymptotically becomes a continuous numerical data.. In the basketball shots example covered by you, the proportion value is based on two things: number of shot successes (p) and number of shots(n). The proportion P=p/n varies with the variation of both n and p (s.t 0
For completion: In the above analysis, I seemed to have inherently assumed a Binary nominal data (shot success or failure) as the underlying source of data for the Proportion of interest. But i guess, it can be generalized to any nominal data that has finite number of categories. Even under this assumption, we'd still have discrete values in our sample space for the Proportion of interest, which will now take the form of "Proportion of which category(/ies)". And the same Proportions will likely start to appear continuous for practical purposes much faster with "n" because there are lot more combinations of the underlying discrete categories dotting our sample space for Proportion.
I read research outcomes regularly, I go straight to the outcome of the research ignoring p values, hypothesis etc bc I don't know what the values mean, but with your teachings I feel I woke up from a great revelation, I see it now, like looking through night goggles in infrared 😂
08:00, Doesn't Continuous data means hence it is more data type will always a floating type. Whereas the number of people on the bus is discrete, or the number of chairs cus there is always be represented as an Integer.
This is the reason I have started my channel here to help others understand mathematics like you. This is very good approach in this field ,DAVIKA Academy which is my channel am working on both pure, applied and statistical mathematics. Once more keep it up let us transform the educational sector .
... Adding to my recent comments: I'm particularly impressed with your superbly clear explanation (and condemnation!) of 'p - hacking' right at the very end of the video; I've just been helping graduate political science students understand the use and suitability of 'Bonferroni' corrections, very closely related to the whole issue of 'p - hacking'! Again, well done, zedstatistics; brilliant!
I wish I found this vid while I was actually in stats. I retained more from this video than I did from the course itself. Pat yourself on the back, this is awesome.
I'm reading all these comments about everyone watching right before an exam and I'm here watching 3 weeks before my statistics class even starts🙃 I'm trying to get all the advantages i can!
The most sophisticated explanation of statistics, I considered difficult to understand about bell curve, you made it so simple to understand easily. Thank you.
I am so glad I came across this video. I'm taking statistics and the instructor basically is letting the book teach us. No lessons. No explanation. Just finish the assignments on time.
Great video. I’ve been exposed to null hypothesis And p-values Many many times and never had it explain so clearly and succinctly so that I actually thought I understood it. I am a poor student or I’ve had bad teachers or both.
Hi Zedstat, if by process of p-hacking you find (by chance) one factor with significance. If you then on that factor collect many many samples that consistently prove that the factor/effect is significant, would you still be doubtful of the process?
GREAT QUESTION. You've actually described a legitimate and robust research strategy. First conduct exploratory analysis to find out the likely factors influencing the outcome of interest. THEN set up a formal protocol around the factor of interest you've selected and (crucially) collect a new set of data to test it on.
The question is about variables. We would say that “height” is a variable. In my mind, “height” does not just represent different values, like 2, 45, or 75. It represents values + units: 2 feet, 45 feet, or 75 feet. However, when we work with variables mathematically, I believe that we think of them as solely representing values / numbers. For example, we will say statements such as: height = 2 weight = height + 4 weight = 6 It seems confusing to me to have to think about variables in 2 different ways. Could you explain if I’m thinking correctly? How do you recommend thinking about variables so that I can do statistics most effectively? Please reply. Thank you
Finally clear, thanks a lot!! I will definitely return to this video when will be confusing about something again, because it summarizes all unclear concepts of hypothesis testing from the very beginning, types of data
Something you can measure ( height , temperature, weight) is continuous and something you can count ( number of players in a team) is discrete . Thats the easiest way I can think of in differentiating continuous and discreet data.
You are just an amazing teacher, I'm a non-statistic person who wants to learn statistics and this video made me breathe easy. Happy subscriber and God bless you.
Super interesting! Thank you so much for making this video. I am totally new to statistics and this video helped me a lot. I'll watch the other videos too!
This is a fantastic overview of statistics! I feel like I'm listening to a great story, and cant wait to hear all the other chapters. As a college student enrolled in stats, I am so glad I stumbled across this video! Your narration manages to take the painfully mundane content of statistics and turn it into riveting story. Thanks to you, the probability of me passing the class just skyrocketed. :)
I agree, I was impressed by this video as well.
This is impressive, I have a similar challenge
You got to be kidding right? This only makes sense if you follow sports at all which I do not. Also, I do not understand the term, “discrete“ unless you explain it to me. Which you didn’t. Not trying to insult a video, just trying to point out that understanding statistics or any mathematical concept I think it depends a lot on one’s frame of reference. For those of us who hear nothing but static the moment conversation turns to sports, I’m thinking there has to be a more generalized frame of reference that more than sports fans understand. And on that very point, how is a player’s position on a team “ordinal” - wait, I’m already asleep. 😴Finally, it would be helpful to understand the overall aim of statistics first rather than diving into the terms immediately. I don’t care about terms unless they lead me to a next stop in the overall direction of where I’m going, and I never get into a car when I don’t have a general sense of where I’m actually going. Because why would I do anything where I don’t have an overall sense to the point of it, actually? My 2 cents, because I do find statistics to be fascinating, even if I’m only minimally good at them, and I was so hoping to like this video.
The Basis of Statistics & It's Technique
th-cam.com/video/mC4cWH7SavY/w-d-xo.html
YC uyoyoioyuoyouyyyoy up put it to you and the next items up poop be put in the fridge raider
"perfect for those just enrolling in a statistics course"
my final is in 2 hours
oh ma god. mines on wednesday. tell me how it went when youre done.
@@lea9720 mine is tomorrow good luck guys
EDIT: Forgot to update but.... I got a B!
Update us on how it goes! :D
Mines tomorrow I’m scared 😭😂
@@SIR3NWINS same here ahahahaha
"The idea here is for you to develop your intuition around statistics..." I'm all ears. I also have an exam tomorrow.
I have good news! I passed my finals and did very well. Thank you so much!
@@DaarinaAC632 so happy for you. I hope I pass oh my im so stressed
Hope I pass mine too lol. Fingers crossed 🤞🏾
My exam is today☺️
My exam is tomorrow, let's gooooo
I spent an entire semester crying my way through trying to understand Statistics. Literally sobbing as I was studying. 4 years later and I'm retaking the course (someplace new) and found this video. Suddenly all of the previous class makes sense and I can see what the Prof was trying to do... he just couldn't present it in a way that made any sense. Always in the weeds of details without an overarching idea of what I was looking at. Thank you so so much!
Yep, too many stats courses, teachers and books, focus too heavily on the math ( and the jargon of assumptions) without first establishing the general principles of what stats are about. Therefore some students get put off by finding the math difficult. Add to this the usual time limits set by most courses etc, and it's no wonder why many ( a high stats %) get put off from learning stats.
Science is also often taught in ways that put people off. First and foremost the principles of Science ( the philosophy regarding why we use the Scientific methods) should be learnt, rather than just try to cram peoples "heads" full of Science based facts. Once the value of those Scientific methods are established, people will more probably value Science and Stats ( rather than, for example, incorrectly believe that opinions are comparable to empirical evidence).
Yo me too! All the way through high school and uni I was convinced I was stupid - turns out just poor communicators teaching me math.
Me right now, second time taking the class, first in high school and im doing a bit better in college. The final determines whether i pass or fail
MY GOD! I am not halfway through my class, but, you just explained EXACTLY how I feel in this class.
@@eddiepraxedis4473 BRO SAME!
I almost quit my statistics course because I was so lost. This and your other videos are definitely helping me!
Don’t give up 💪
I am at this point right now and I feel like giving up, this is my 5th week in school and I'm lost in statistics @@ad-renaline2136 😪
0:00 Introduction
- 30 minute summary of statistics
1:18 Data Types
- numerical (continuous and discrete) or categorical (nominal and ordinal)
Steve Curry example, nominal data. Is a proportion considered discrete or continuous numerical variables?
8:04 Distributions - Probability density function - P.D.F. Normal (bell curve), uniform (bi-modal), left and right skew -shapes. PDF is unique to an event and based on data. Probability of choosing data (a player) of a certain attributes (conditions on dimensional attributes - a certain height). Sampling , averaging many events, can be viewed as having a steeping PDF and a lower variance.
13:51 Sampling and Estimation - more data provides better confident interval. Sampling
mean - average , must be numeric (mu) Makes sense that none numerical data uses mode and median x (with bar hat)
standard deviation (row) - difference from the average, on average s
proportion (pi) - is a measure of data, whether nominal or numerical. p
correlation (row) - the connection between dimensions r
gradient (beta) - change between dimensions. Regression b
(theta) - general variable. (and the greek with statistic and english for probability of sampling)
22:30 Hypothesis testing - binomial distribution (PDF) Since 50% is null, 7.5 is the likely outcome, middle of the bell. less than 5% would reject the null. In this case, we'd not reject. We don't accept or prove the alternative, we reject or don't reject the null. Legal system, innocent until proven guilty.
31:28 p-values - our test statistic is 30.4% (area under 9).
35:35 BONUS SECTION: p-hacking
I find it interesting that all data is conceptual, or also referred to as nominal (I don’t mean or in the set theory this OR that categorization way, just as a different name for the same concept way). That includes data that is numerical. Just because we assign a character "3," doesn't make 3 not a name. Numerical is a name that can be used as a measure. Measures are about numerical comparison. Both the concept of numerical and measure are tied to counting, while nominal is not. When we say nominal data, we literally mean, a name of a concept, but we also mean "not a measure” or “Lacking ordering of elements.”
(Ordering is always a numerical property of “distance” but distance is not always a property of ordering, as nominal data can be given structure and ordinality.)
The measure (or what you are calling a proportion of data - where we use numerical values as a comparative evaluation) of a dimension of all or any data (to my understanding - correct me if you disagree with my narrative, I'm only starting to learn about data) can have the attribute (a specific entry of a dimension) of "numerical" (As opposed to "nominal," but if we are being honest, "non-numerical" is more descriptive).
I'd argue that reality is continuous. Discrete is the way that we process or model reality. The same way that we process continuous functions that represent physical signals in reality (currents, voltages, energy, ...). We discretize to understand and find meaning. The ship of Theseus is a good example of a continuous reality. I'd even go as far as to say you can't process data without discretization. Calculus and differential equations attempt to approach this continuous reality, but show that the reality is right out of our reach for understanding as we discretize our understanding. Continuity is a form or experience and reality, and, as such can't be presented unless in a discrete way. Other narratives around data types is: 1. Nominal, 2. Ordinal, 3. Interval/Ratio.
Both measures (proportions), produced from the given data set, are example discrete dimensions of data. I'd argue that counting is not continuous. Saying numerical data has an attribute of "continuous," isn't illogical. If it is, I'd challenge you to provide an example. To say that data has an attribute of nominal (non-numeric) or numerical, is logical. This topic deserves a whole essay to do it justice.
It is worth noting that there is value to comparing the concept of continuous (changes don't occur instantaneously) and discrete, but I suspect we are a little too liberal in our use of saying numerical data is either continuous or discrete. I don't think it is an accurate statement, but it is part of how we communicate meaning. We should change that.
Another observations are that the numerical is also ordinal data and that ordinal is a subset of nominal and numerical is a subset of ordinal.
The key with math and legal and politics and life, is understanding what and how you should look at something. That is the toughest part about math. Identifying what you are looking at and how you should conceptualize it. Example. It is a function, an equation, and/or a relationship. I suspect it is much the same with Stats and Prob. Great Philosophical video. I'll have to check out some of your others.
www.zstatistics.com/podcast
Chris Cf ockrell
Chris Cockrell , Just a word of caution... if you are quite sure of yourself, write a lot. If not, consider not writing so much. The possibility of a mistake grows almost surely as a function of your sureness, but little helps you if you make one.
is it discrete or continuous ? i guess it's discrete.
@@BruceNitroxpro So think little, write very little, so you make no mistakes? So you aren't wrong? I'd rather risk being wrong.
If you disagree with my thought, explain.
I could be totally wrong. I take no issue with that. Education is too often about regurgitation, not understanding. It often lacks thought and consideration.
@@tarunupreti6387 Why? Do you think time and space (geospatially) are discrete?
Chris Cockrell , Not sure you are even replying to one of my comments. I can't find it if you are... If you are just posting an opinion, go for it. I agree with the part I see.
As a former college instructor of statistics, I must complement the narrator of this series on the value of the short course for the average viewer.
Sir can u please help me with the std deviation considered in the baseball example. , ?
to the former college instructor - compliment*
hi, i did math stats, anova and regression end of last year, this year i did stochastic and im currently doing time series. I want to deepen my stats knowlege any sugestions on which courses i should do next? could you give me a map on where to go next?
Me, 45 minutes before a 1.5-hour university exam on statistics.
♫ Where is my mind... ♫
Howd it go?
try this trick and spin it
PIXIES
@@chidiasoh lol what a creep
@@chidiasoh you have 10 years old video wtf
I love the calming voice and the lack of a music track in the background, it makes the video so much easier to listen to. So many of these videos are nearly impossible to listen to due to invasive music.
Yeah what's with that.
Yeah, so true
...and don't forget the ugly noices of some specialists and the fast running speeches of them - so you just can't follow, get only fragments and bad feelings.
I became obsessed with NBA statistics when I was 12 after buying my first basketball cards. There was a problem, some cards didn't include averages, only totals. There were cards that included less seasons, but included career totals, etc. So I to applied the basic statistics I knew to get the missing info (91 pre-internet living in Mexico btw) and I got most of the concepts from doing that, but without the terminology of course. So now I'm trying to learn more so I can fill in the gaps, mainly because I have an interest in risk theory, probability and all things related. Your videos are great and easy to understand, thank you so much!
I'm a recently retired British (high-) secondary school teacher of mathematics; your approach appeals to me very much, and is similar to way I have taught the subject. I find your explanations really engaging, relevant and crstal clear! Well done, and thank you for helping to spread a better understanding of mathematical ideas and techniques.
I've zero understanding of what statistics really is before watching this video.
All beginer should watch this!! Best statistics overview ever!
It feels like listening to a story rather than a class. Great work. thank you
Zed, you are an amazing teacher/ professor/ trainer, I must say. You took even challenge so humbly, yet you didn't miss your purpose, i.e., to teach us :) Kudos to you! Im happy I found a guru like you! Now , to answer your 1st question in data for proportion, I think it's continuous data.
I think so too! Hoping somebody smart answers to tell us yes or no soon T_T xD
This is a very helpful video for someone who has been out of school for over 10 years. I am currently pursuing MSN and MBA. This audiovisual presentation/video suits my learning style as I am not a fan ever of reading thick textbooks. Maybe articles and short paragraphs I can read but majority, I like watching videos, looking at diagrams and pictures. I am an auditory, visual and kinesthetic learner. I write while I listen to the narration, and this helps me a lot! Thank you so much.
do more of these, I love watching speedrun explanation videos the day before the exam, feels good and you reabsorb so many things just when you hear it from a different source again! friendly greetings from Germany!
Thank you for this. I'm back in school after nearly 20 years, and I honestly don't know how my young brain was so much better able to understand these concepts. I find that I need a lot of supplementary support now, and this video has been very useful.
As someone who needed a review os statistics and wanted to know the concepts and not the fórmulas (My teachers used to just throw formulas) I GREATLY apreciate this video. I am not even that much in to statistics but have my sub man!
Even though this video is just an inch longer than 30 mins, it is still a huge, gigantic feat - you have done the impossible once again, Justin. This is a Guiness World Record-worthy effort. Cheers to you 🍻
Thank you. I'm a BA in psychology, but it's been a while and now getting ready for an MA entrance exam this is a really good intro before all the equations come into the picture.
Gee I wish I'd found this video several weeks ago when I first started college stats class online. thankfully I have an amazing instructor who understands my frustrations and has helped me sooo much in this class. But I wish I'd found this back 7.5 wks ago when or even before i started this class. this makes so much sense now! Thank you!
statistics in 30 mins... ok I'll challenge myself... Proceeds to set speed to 2x
It's cool I only read every second word of your comment
@@zedstatistics lol
lol i’m not a native American and i also watch videos 2x, happy to find it’s not me alone
I am on the verge of making a change in my job role that involves learning statistics, among a whole bunch of other things, after a decade-long career in ITSM. I was uncertain as to whether I would enjoy spending time going deeper into statistics again. This video is what I needed to get started. Thank you for sharing your knowledge. I have subscribed to your channel, and looking forward to seeing more content in the future. 🙏
p-hacking is an interesting way to figure out things that are actually worth going out and doing MORE research on. that's kind of what is neat about data aggregation... there may be emergent trends or effects that can indicate real problems in need of more exploration.
For example, in my home region they found that certain area codes were connected to higher cancer rates. This doesn't mean that the area code itself caused cancer, but that there was some phenomenon that resulted in increased rates of cancer. More research revealed it was two key factors: higher rates of persons with sedentary jobs (truck drivers, etc), and higher rates of smoking in a cultural group that was more dense in specific areas. These discoveries made it possible to design meaningful health interventions that could specifically target those groups in that particular sample.
As a Sports Analyst, this video is awesome. Great job. This is inspiring a whole new generation to learn more about statistics. Thanks mate!
I was apprehensive ( 2nd time taking class) to start this class in the Fall which I need to take to graduate. After listening to you explain in great detail, I'm not feeling apprehensive anymore. I thank you for this video.
I always felt bad for this channel having very few viewers compared to its content. I am so happy to see that such a video has 1M views. I hope it gets 10M+ views.
Me watching this an hour before my final 🤦♀️
How did it go?
@@karag4487 we may never know...
I watch these types of videos *after* my exams
@@philnightjar1971 5head
@@philnightjar1971 🤣🤣
It's been 25 years. I need a refresher
Goddamn, i spent 4 hrs struggling to know the exact and basic meaning of hypothesis theory and p value, thank god i found this today!
Thanks a lot!
Great video. Mathematically speaking, Steph's shooting percentage is a ratio and not a proportion, it's the ratio of 3-pointers made divided by total attempts. A proportion is when two ratios are equated, for example, how many 3-pointers is Steph expected to make if he continues to shoot say another 250 3-pointers. Also, in the scientific method, an effect is hypothesized, then experiments are conducted, and if the hypothesis appears to be valid, then the hypothesis is advanced to a theory.
It is funny that statistics teachers tend to use baseball or some other sports examples rather frequently when only a small percentage of learners can even understand those sports enough to make sense of the statistics that is being taught based upon those examples..
A "small percentage" of learners can understand sports? What bad statistics are you looking at? Or are you simply falling victim to a laughably revealing case of Observer Bias? Brother, do yourself a favor and pick up a sport to watch, you'll find it makes it easier for normal people to like you.
@@partymcfly5549 You need to get out more, instead of being a couch potato absorbed in sports on TV.
I dont relate to any sport either, so these examples are difficult for me too. And sports isn't the only thing that gets you "out in the real world". Maybe examples of everyday chores or student life related examples would make more sense. @partymcfly5549 Take a look at your comment... It itself is Observer Bias... Quite laughable
I have a psychology test tomorrow - cannot get to grips with the damn stats even though we have covered it many times in my degree! Thanks so much for your explanation! And now after knowing nothing about NBA I can talk to my NBA fan friend about Steph Curry! Sweeeeet :)
Wow, you have got some great skills in breaking down complex topic and explaining it to others in simple way. Thank you.
Great video! Like that you used NBA as examples. Keeps things interesting.
Thanks for taking this challenge and sharing with us such a useful video! :-) Can you please also consider making a video with the *mathematical fundamentals of statistics* that would create a good *foundation* for us to be able to *solve any statistics problem?* I will be taking a *college statistics class* but I'm nervous because I've never been good at math. I really wish I could see a well-structured video that teaches me *the core basics* that will help me *perform the mathematics of statistics* and do well in a college statistics class. It can be a half-an-hour class but it can also be one hour or more, as long as the fundamentals are thoroughly covered in a *structured* way. I would still consider it *ambitious* even if it's a few hours long video because it's still much shorter than a college statistics math class, which tends to be 3-4 hours per week, and it lasts for many weeks. I want to know *the math of it* but I also want to see it explained with actual *real world examples.* I have to learn how to do the math in order to do well in the class but examples and visuals help a lot too for remembering things. A combination of both would be best, although the math part is somewhat more important because it can be a lot harder to learn it.
I really hope you consider making a video like this because it would help a lot. It would help make my semester feel much less tortuous, if I can prepare ahead of time.
Thank you so much for your time! :D
It would basically take an entire college course to explain all of that properly. That's why college course are the length they are.
Just a note that at 31:58 the slide says "ASSes" instead of "assess"
Statistics grad student here. Great video. Obviously there are a lot of details and examples worth covering also if a person really wants to learn the material well, but this is a great overview/review. Learning statistics will definitely change the way you think and function in the world.
Exactly i have been a while thinking how statistics in my country is so neglected in school education although we make as humans a lot of interpretations based on statistics and counting. As a result we fall in a lot of fallacies. Also although we have a lot of data which should help us unconsiously think using law of large numbers but will fail because we care about our emotions and desire driven more than thinking logicalll.
This made everything much clearer. I am a visual learner do I desperately needed this!
I feel ya completely.
7:48 ANS : A proportion is a discrete data type as the underlying data it is calculated from is also discrete.
Very well instructed Sir!
I am going back to school to further my career in business.This video helps a ton with understanding the basic concepts and as you said "building your intuition around statistics"
I will send links to all my friends. You rock and just gained a Subscriber. 🍻
Thanks, EB! Good luck in your career :)
Trying to wrap my head around the p-hacking subject (36:13):
Study 1:
So if you conduct a research project where you only look at a single effect, you wouldn't expect your calculated p-value to be less than 0.05 if the null hypothesis is true, because that would happen in only 1 out of every 20th time you repeat your study. Thus, if I observe p < 0.05 for my single effect, the odds are in favour of this being a true signal that the null hypothesis is wrong. However, if I (or any of my peers) repeat my project 19 more times, I would actually expect one of the tests to result in p < 0.05 even if the nullhypothesis is true, just because that's the actual definition of the p-value (given a significance level of 0.05).
Study 2:
An alternative way research is conducted is to collect a lot of data, and then test for several effects. Every single effect you test will be the equivalent of conducting study 1. So, by testing 20 different effects in your data set you would actually expect one of your effects to have p < 0.05 even though the null hypothesis for that effect is true (aka. falsely concluding the null hypothesis is wrong).
Is this correct?
one of the most rewarding teaching videos I have ever seen on youtube. Respect
You read my name, boy
it is a huge difference watching this in a relaxed state and after already having completed stats 1 and 2.
this video is amazing at clearly explaining statistics! it would be great to have a video with the maths to accompany it!
Well done- I have read so much in my graduate text book on Stats, but the video simplifies 500+ pages of reading into 45 min. Thank you well done.
Thank you, thank you for easing the pressure in my brain that is new to statistics. I have read a few chapters, but your video crystalizes basic statistics for me.
Is a proportion a discrete or continuous numerical data?
Proportion is a percentage or part of a whole, right? So two thoughts...
1. Percentages seem to be continuous, because you can have almost any number along the line from 1 to 100 %. For example, half of the cookies in the cookie jar could be gone, but there are crumbs so these might count towards the percentage if the way we are measuring is by weight. (I know more about cookies more than basketball obviously).
2. But then it also seems that it is discrete because it can only be some part of the whole, like 1/10th is always .1 or 10% of the whole (10). Or in the case of the cookie jar, there is still 51.25 % (with the crumbs), but this is a discrete proportion of the whole?
Where is my thinking off here?
(I wish I had a stronger intuition for numbers and I’m trying to built it, but this whole categorizing data seems slippery to me still).
After learning most of these concepts separately, it's so satisfying to watch how you went over them in such a simplified and comprehensive way. Thank you so much!
I am a medical student who wanted to get the hang of this, for my research projects and I can safely say with a p value of 0.05 that I clearly understood these wild concepts.Thanks to my guy!^^
Great video but choice of sports was wrong , maybe Football or Lawn Tennis which is more universal could have been a better sports. Had no clue what "3 pint percentage " is in Basketball
I was watching this video to brush up on statistics concepts I have studied before. It was an amazing refresher and also gave me new perspectives to look at certain concepts and see them in a simpler and more natural light. Thank you so much!
do we have 50% probability of making 3 points by chance ?
Hi E.D.! Steph Curry is considered to be the best 3 point shooter of all time. His all time percentage is 43.5%. So no, 50% is probably a little optimistic for most mortals. Though I swear I used to bomb threes in my backyard closer to 70%.
@Kenn Klein can we connect i am stat tutor too, we hope make collab
@@zedstatistics There is a huge difference between shooting during practice and shooting during the game. It would have been a great analogy for why understanding research is important. A lab test needs to know what it is testing before it can test it.
@@zedstatistics Ah but the backyard and the arena are two very different places 😏
This is mind blowing video for the beginners. I loved watching it. You have made statistics so simple to understand. I have started loving this subject because of you made it so easy to understand. Thank you and May God bless you Justin n Team.
This just helped me immensely! Soo sooooo grateful for your video! Thank you very much! And I wholeheartedly agree with Jennifer's comment that's pinned. I feel like I'm listening to a story, rather than learning theoretical jargon. I understand the subject so much better now!
Yupp
I have started learning Data Analysis. And was looking for something short to get into the game of statistics faster, this video was really motivating. Thank you very much for your effort. I really admire your effort for making this so simple.
How's the data analysis going?
The most important massage is on the end about p hacking. Very well explained. I kind of knew what it is about but could never explain it to others. Thanks!
Statistic is how to know things happened in any population without doing study on all members of population because of lack of sources. So, you have to design method to study sample of population but your result should represent population.
you're a brillianttt !! I'm a pro card counter and we use statistics a lot. I've never loved maths or statistics in my life, I just thought it was cool when I was younger age but wasn't my cup of tea. But now I was interested and started to research and bumped into your video which made it even more interesting!!!! Thank you so much for your hard work and very simple explanations on all of that!!!!
I got a problem for you that I'd love to hear the answer too. You see a scorpion on the arm of a wicker couch crawl into the arm of the chair. A day goes by. The area is an enclosed rectangle around 7ft by 24 feet. 8ft ceiling. With a small cat door. There's a few tables. Some cabinets, the love seat that the scorpian crawled into, a wooden reclining chair, and an office chair. What's the percentage that the scorpian is still in the love seat? Also, which chair would be the best to sit in and not get got by a pokie grabbing thing of pain?
You have become a part of my study material for an exam in my Data Science class, we did not review much of statistics, only that we are expected to know it already. Many thanks for your contribution!
Because my professor was struggling from some personal matters, we only got halfway through the syllabus. I'm glad that there's still videos online like this one where I can still learn those important skills
6:06 six minutes just passed and he lit his first cigarette
@@saimaacademy5537 LOL
You got busted dude, anyways nice video thx
@7:50 Is Proportion a discrete or continuous numerical data?
I believe it is a discrete numerical data that asymptotically becomes a continuous numerical data..
In the basketball shots example covered by you, the proportion value is based on two things: number of shot successes (p) and number of shots(n).
The proportion P=p/n varies with the variation of both n and p (s.t 0
For completion:
In the above analysis, I seemed to have inherently assumed a Binary nominal data (shot success or failure) as the underlying source of data for the Proportion of interest.
But i guess, it can be generalized to any nominal data that has finite number of categories.
Even under this assumption, we'd still have discrete values in our sample space for the Proportion of interest, which will now take the form of "Proportion of which category(/ies)".
And the same Proportions will likely start to appear continuous for practical purposes much faster with "n" because there are lot more combinations of the underlying discrete categories dotting our sample space for Proportion.
I read research outcomes regularly, I go straight to the outcome of the research ignoring p values, hypothesis etc bc I don't know what the values mean, but with your teachings I feel I woke up from a great revelation, I see it now, like looking through night goggles in infrared 😂
08:00, Doesn't Continuous data means hence it is more data type will always a floating type. Whereas the number of people on the bus is discrete, or the number of chairs cus there is always be represented as an Integer.
Excellent analogies. Really helped bring back the stats I learned many years ago. Thanks.
Masters stats student here. Took a semester break cuz I went broke and had to work full time. Awesome refresher. Thx!
Gonna watch this before my AP test. Someone comment so I remember to edit with my score!
my ap test is tomorrow so i’m in the same boat ✌️✌️
@@shreenivedithajayakumar bruh 🤞🤞🤞 good luck
This is the reason I have started my channel here to help others understand mathematics like you. This is very good approach in this field ,DAVIKA Academy which is my channel am working on both pure, applied and statistical mathematics. Once more keep it up let us transform the educational sector .
... Adding to my recent comments: I'm particularly impressed with your superbly clear explanation (and condemnation!) of 'p - hacking' right at the very end of the video; I've just been helping graduate political science students understand the use and suitability of 'Bonferroni' corrections, very closely related to the whole issue of 'p - hacking'! Again, well done, zedstatistics; brilliant!
I wish I found this vid while I was actually in stats. I retained more from this video than I did from the course itself. Pat yourself on the back, this is awesome.
I'm reading all these comments about everyone watching right before an exam and I'm here watching 3 weeks before my statistics class even starts🙃 I'm trying to get all the advantages i can!
Same here!
👻
I thought I was the only one 😂 cuz I’m not trying to get in there lost lol
SAME! 😭
Im watching a week after my finals just to confirm if I'll ever gonna get a decent score on the finals 😂
This is the Macro statistics video I've been looking for!
Had exams in 40 minutes, thanks this helped.
The most sophisticated explanation of statistics, I considered difficult to understand about bell curve, you made it so simple to understand easily. Thank you.
Really appreciate this! Coming to my final in stats, so it's great to have a condensed overview just to remind me of the concepts
I am so glad I came across this video. I'm taking statistics and the instructor basically is letting the book teach us. No lessons. No explanation. Just finish the assignments on time.
Great video. I’ve been exposed to null hypothesis And p-values Many many times and never had it explain so clearly and succinctly so that I actually thought I understood it. I am a poor student or I’ve had bad teachers or both.
One of the best Teacher of any subject that I have listened to. Well done Sir!
I'm taking stats now, and we've just gotten to hypothesis testing. This is so helpful! Thank you!
if you put this on 2x speed, it only takes 21mins. Nailed it :o)
🌹🌹🥳👍
Here reviewing basic stats before I start my new job as a Data Analyst, thanks for this!!
Hi Zedstat, if by process of p-hacking you find (by chance) one factor with significance. If you then on that factor collect many many samples that consistently prove that the factor/effect is significant, would you still be doubtful of the process?
GREAT QUESTION. You've actually described a legitimate and robust research strategy. First conduct exploratory analysis to find out the likely factors influencing the outcome of interest. THEN set up a formal protocol around the factor of interest you've selected and (crucially) collect a new set of data to test it on.
The question is about variables.
We would say that “height” is a variable. In my mind, “height” does not just represent different values, like 2, 45, or 75. It represents values + units: 2 feet, 45 feet, or 75 feet.
However, when we work with variables mathematically, I believe that we think of them as solely representing values / numbers. For example, we will say statements such as:
height = 2
weight = height + 4
weight = 6
It seems confusing to me to have to think about variables in 2 different ways. Could you explain if I’m thinking correctly? How do you recommend thinking about variables so that I can do statistics most effectively?
Please reply.
Thank you
Wow, excellent video, every time I see one of them, I feel more intelligent afterwards.
Finally clear, thanks a lot!! I will definitely return to this video when will be confusing about something again, because it summarizes all unclear concepts of hypothesis testing from the very beginning, types of data
Me 30mins before exam can't be satisfied
im enrolling myself so decided to look tings up, ty ty
time to go fail my exam then
This is probably one of the best TH-cam videos
Thank you man! Very informative video for those who have just started studying statistics!
Something you can measure ( height , temperature, weight) is continuous and something you can count ( number of players in a team) is discrete . Thats the easiest way I can think of in differentiating continuous and discreet data.
Why didn't I watch this when taking my econometrics....very informative videos!
You are just an amazing teacher, I'm a non-statistic person who wants to learn statistics and this video made me breathe easy. Happy subscriber and God bless you.
Super interesting! Thank you so much for making this video. I am totally new to statistics and this video helped me a lot. I'll watch the other videos too!
one of the best videos.cleared all my doubts
I like the way you took baseball players for an example! I felt more related :)
Just started Statistics for my BSN in nursing!! So glad to have found this! Thank you!🙏
Great video!! I wish I had you for my lecturer for stats 101, many moons ago!!
I wish I had him as my lecturer for 601 and 602 stats....
This video has the best explanation of Hypothesis Testing and p-value. Wish I would have had access to this video 15 years ago.