🎯 Key Takeaways for quick navigation: 06:34 📉 *Type 1 Error: Rejecting the null hypothesis when it is true leads to a Type 1 error, often depicted as a false positive.* 13:13 📈 *Type 2 Error: Accepting the null hypothesis when it is false results in a Type 2 error, commonly seen as a false negative.* 25:32 📈 *Two-Tailed Test: When investigating if a college's placement rate is different, it's a two-tailed test, checking both greater and lesser than the standard rate.* 27:35 📉 *One-Tailed Test: If the question changes to checking if the placement rate is greater than 85%, it becomes a one-tailed test, focusing only on the greater side.* 32:00 🎯 *Point Estimate: In inferential statistics, a point estimate is the value of any statistic that estimates the value of a parameter, providing an approximation of the population value based on sample data.* 32:27 📊 *Point estimate is the value of any statistic that estimates the value of a parameter, like estimating the population mean.* 33:30 📏 *Confidence interval is determined by the formula: Point estimate ± Margin of error. Margin of error accounts for uncertainty in estimating the population mean.* 35:13 📈 *Example problem: Given population standard deviation, sample size, and mean, construct a 95% confidence interval using the z-test formula.* 36:33 📉 *When population standard deviation is known, and sample size is greater than or equal to 30, use the z-test formula for confidence intervals.* 49:48 🧮 *When population standard deviation is not given, and sample size is less than 30, use the t-test formula for confidence intervals.* 51:05 📚 *Example problem: Given sample standard deviation, sample size, and mean, construct a 95% confidence interval using the t-test formula.* 59:11 🎉 *Completion of confidence interval explanation and problem-solving using z-test and t-test.* [01:07:58 URL](ruclips.net/video/PbguWyuZ4Ts/видео.html) *📊 When conducting hypothesis testing, the third step involves stating the alpha value, often set at 0.05.* [01:08:28 URL](ruclips.net/video/PbguWyuZ4Ts/видео.html) *🤔 In the fourth step, defining the decision rule is crucial. For a two-tailed test with an alpha of 0.05, the critical values are ±1.96 for a Z-test.* [01:10:50 URL](ruclips.net/video/PbguWyuZ4Ts/видео.html) *🧮 Calculating the Z-test statistics involves the formula (X - μ) / (σ / √n), where σ is the standard deviation. Always use root n for sample data to account for the standard error.* [01:16:39 URL](ruclips.net/video/PbguWyuZ4Ts/видео.html) *🚫 If the calculated Z value falls outside the range of -1.96 to +1.96 based on the decision rule, reject the null hypothesis.* [01:28:29 URL](ruclips.net/video/PbguWyuZ4Ts/видео.html) *🧠 Applying the learned concepts to a practical problem, rejection of the null hypothesis indicates an increase in intelligence in this scenario.*
Bank - ATM Opening in Given location -- 1. Collect the Population Mean - Personas Visiting the ATM - 2. Select the Sample Mean - typical > 30 sample persons vesting particular ATM 3. From the Population , we can find the std deviation 4. Based on the alpha - we can find the confidence intervals 5. calculate Z- Value 6. Based on the z -value - we can accept/reject the Null hypothesis
I completely agree to you and I was thinking the same way as you but I got stuck at the situation that what would be the null and alternate hypothesis in this case? Can you help me by answering that?
After watching all statistics 7-day live sessions and now I have got so much confidence in stats, and I am very sure that I could learn data science subject. The way you teach is very clear and absolutely very easy to understand which I didn't find in my university. A Big Thanks to you Krish Naik ji for your great effort to help me and many students with your knowledge to shine their future and it is much appreciated. 🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏
Thanks for the video lectures. You have very clearly explained the concepts from basic to advanced level. 👍. Thanks for all your efforts. Much appreciated
06:34 📊 Type 1 error occurs when we reject the null hypothesis when it's actually true, leading to a false positive conclusion. 14:28 📉 Type 2 error happens when we fail to reject the null hypothesis when it's actually false, resulting in a false negative conclusion. 19:04 📝 In a hypothesis test, a one-tailed test focuses on one direction (e.g., greater than or less than), while a two-tailed test examines both directions. 26:15 🧠 Understanding the context of a question is crucial to determine whether a hypothesis test is one-tailed or two-tailed. 32:41 📏 Point estimate in inferential statistics involves using sample data to estimate population parameters, recognizing that the estimate may not be exact but approximate. 33:01 📊 Point estimates in statistics provide values that estimate parameters, such as the mean, from a sample. 33:30 🛠 Confidence intervals are used to estimate the population mean, typically calculated as point estimate plus or minus the margin of error. 35:56 📈 When given the population standard deviation, the z-test formula is applied to calculate the confidence interval. 37:01 🧮 Given population standard deviation, sample size, and confidence level, constructing a confidence interval involves calculating the margin of error using the z-test. 51:23 🧪 When population standard deviation is unknown, the t-test is used to calculate confidence intervals, with the sample standard deviation and sample size. 01:09:26 📊 Z-test helps determine if a value falls within a specific range by comparing it with critical values like 1.96 for 95% confidence. 01:11:05 📈 Z-test formula: (x - μ) / (σ / √n). Standard error (σ / √n) becomes relevant for larger sample sizes to match population means. 01:15:06 🧮 Test statistic computation for Z-test: x̄ - μ / (σ / √n), considering sample mean, population mean, and sample size. 01:16:53 🧠 Rejecting the null hypothesis in hypothesis testing suggests an effect, like increased intelligence due to medication. 01:26:41 📉 T-test formula for test statistic: (x̄ - μ) / (s / √n), with x̄ as sample mean, μ as population mean, s as sample standard deviation, and n as sample size. 01:28:29 🎓 Rejecting the null hypothesis indicates an increase in intelligence, highlighting the importance of hypothesis testing in real-world scenarios.
🎯 Key Takeaways for quick navigation: 20:25 📊 Two-Tailed Test: Checks if the rate is different, greater, or less. 29:19 🎯 Confidence Interval: Uses point estimate, a statistic, to estimate a parameter within a range. 30:19 📊 Confidence Interval Basics: Calculations involve finding upper and lower bounds to estimate parameters. 32:27 📊 Point estimate is the value of any statistic estimating a parameter, like the mean. 33:30 📏 Confidence interval: formula - point estimate ± margin of error, providing a range around the mean. 35:13 🎯 Given a problem statement, population standard deviation, sample size, alpha, and mean, construct a confidence interval using the Z-test formula. 50:19 🔄 If population standard deviation is not given, use a T-test for the confidence interval formula, even if the sample size is less than 30. 01:08:28 📊 Specify decision rule in hypothesis testing considering alpha and tail type. 01:09:06 🧪 Two-tailed test with alpha 0.05: critical values are ±1.96 for a standard normal distribution. 01:11:19 📈 Standard error in z-test formula: divide by the square root of the sample size (root n). 01:14:11 📝 Z-test formula for test statistics: (x - μ) / (standard deviation / root n). 01:16:26 🤔 Rejecting the null hypothesis in a z-test implies the medication had an impact; direction depends on the z-score sign. Made with HARPA AI
After watching this playlist till now. I couldn't understand why my college teacher does not teach me like this. If my college teacher teaches me like you sir I will never bunk the classes.
HI Krish, This live sessions has been the most useful videos i have found in RUclips. i had viewed lot of other channels, but you the way you explain with huge patience. much appreciated. Thank you for providing this for free. thank you a ton, and you are like andrew ng for me.
If anybody know answer of this question so please help me 🙏 1.what is use of confidence interval ? 2.population standard deviation is given so we use z test otherwise t test, is that right
@@jemilmangukiya8134 for 1st question confidence interval is used for how "good" an estimate is and for 2nd question yeah if u don't have population standard deviation we go for T test
basically first we have to decide which is actual side and which is predicted one .. let's say vertical side actual and horizontal is predicted then .. 1st block will be TN , second block in horizontal will be FP , next line 1st block FN and 2nd block TP .. 1 0 1 0
Hello Krish., today I saw you video really amazing.realy the best to understand and learn for beginners. And thank you soo much for providing such a great resources... I woked in different sector and have gap. Now I am mother of twin babies. Have a long gap .I don't have time. Still making myself free to study.... But still following your videos and practicing.. Am I eligible to get job in 35+..???can you guide me.. How to follow you and how to practice in better way.. ?? Hope you will replay me...
I have a doubt. First we used 1.96 in confidence interval formula, and then got the value of confidence interval but in the later part of video during hypothesis testing(z test) we directly used 1.96 as confidence interval without substituting in formula. Which way of calculating confidence interval is right?
Till day 4 it's understandable for non maths or science background students but after dy 4 m totally confuse u can check your comment section by counting the dy 1 comments nd now on day 4 it's decreasing that means only those students are with u till now who are already knows the basic or intermediate level of stats i thought this video series will help me but I not found usefull please help
Sir when will Be the new fsds course is coming...please make a new summer batch sir nearby april or may ...😭😭 Some of the people missed chance this time
Timing 33.20 you are saying sample mean can be greater than population mean how can that be then sample size has to be greater than population size which can't be true....please explain
Hi, In the part 1:00:20 of the video, the curve drawn is according to the 2 tail test. So shouldn't we take the value for t0.025 from the 2 tails in t table i.e. 2.492
Type 1 and Type2 errors: suppose, we think about hiring process, any student selected in company with support of relatives but actually he is no able to work on that position, even he not have any skills-- Type 1 error. In case of other student , he is not selected in company but he has all skills realated to any position-Type 2 error.
Doubt: At 1:10:36 you only took Z(alpha/2) and did not multiply it with (sigma/underoot n). So Z(alpha/2) value is common for all problems/cases/scenarios?
when solving last problem for 't test' in live, why did he take mean of population (mu) = 100. we are going for 't test' as we don't have (mu). can anyone answer if understood?
can someone tell me how do we conclude as medication has improved on rejecting null hypothesis? Is it because it falls > 2.05 region? how do we know null hypothesis states that it is showing the values for medication not improved on sample?
Because of of the pop mean we constructed the CI which in our case is 100.So this is considered as true.But after one sample z test we find that it is wrong since our obtained value falls outside the interval.Since the movement is to +ve side,we conclude medication has improved it
@@akashgautam1909 type 1 error meaning - we reject the H0 but in reality it is true type 2 error meaning - we accept the H0 but in reality it is false so let's take an example Our H0 = person is innocent but the court found him guilty, but in reality, he is innocent so this is a type 1 error because our H0 == reality but the court rejected it meaning, FALSE POSITIVE (something is false but we say it is true) in next case H0 = person is innocent and the court found him innocent, but in reality, he is guilty. so this is a type 2 error because our H0 != reality but still court accepted it This means its FALSE NEGATIVE (meaning, something is true but we say it is false) In this case, he is guilty but we say he is innocent
already you are dividing alpha/2 where alpha is 0.05 we get 0.025 in each tail why are you subtracting 1-0.025 for one tail , you can take 0.025 directly as it is for 1 tail. did you take that as we dont have values to check for 0.025 in z table? please respond
See as 14.60>1.96 we concluded that it is beyond Confidence Interval thus rejecting the null hypothesis, similarly this time its 3.65>1.96 so we again will reject the null hypothesis. The question here is has the medication(in case where sample mean is 110) improved IQ or has it decreased IQ!? -Do let me know if you got the answer for this part, I hope you understood the above section to why we are rejecting null hypothesis( which is mu=100)
Outcome 2 should be Type 2 error and Outcome 3 should be Type 1 error isn't it ? as Type 1 => we accept the null hypothesis, in reality it is false i.e. FP and Vice versa for Type 2 (FN) Please correct me, if I am wrong.
You are correct , you think of it as a severity as well , Type 2 (False Negative , escape from punishment ) , Type 1 (False Positive, punished inspite being not guilty ).
🎯 Key Takeaways for quick navigation:
06:34 📉 *Type 1 Error: Rejecting the null hypothesis when it is true leads to a Type 1 error, often depicted as a false positive.*
13:13 📈 *Type 2 Error: Accepting the null hypothesis when it is false results in a Type 2 error, commonly seen as a false negative.*
25:32 📈 *Two-Tailed Test: When investigating if a college's placement rate is different, it's a two-tailed test, checking both greater and lesser than the standard rate.*
27:35 📉 *One-Tailed Test: If the question changes to checking if the placement rate is greater than 85%, it becomes a one-tailed test, focusing only on the greater side.*
32:00 🎯 *Point Estimate: In inferential statistics, a point estimate is the value of any statistic that estimates the value of a parameter, providing an approximation of the population value based on sample data.*
32:27 📊 *Point estimate is the value of any statistic that estimates the value of a parameter, like estimating the population mean.*
33:30 📏 *Confidence interval is determined by the formula: Point estimate ± Margin of error. Margin of error accounts for uncertainty in estimating the population mean.*
35:13 📈 *Example problem: Given population standard deviation, sample size, and mean, construct a 95% confidence interval using the z-test formula.*
36:33 📉 *When population standard deviation is known, and sample size is greater than or equal to 30, use the z-test formula for confidence intervals.*
49:48 🧮 *When population standard deviation is not given, and sample size is less than 30, use the t-test formula for confidence intervals.*
51:05 📚 *Example problem: Given sample standard deviation, sample size, and mean, construct a 95% confidence interval using the t-test formula.*
59:11 🎉 *Completion of confidence interval explanation and problem-solving using z-test and t-test.*
[01:07:58 URL](ruclips.net/video/PbguWyuZ4Ts/видео.html) *📊 When conducting hypothesis testing, the third step involves stating the alpha value, often set at 0.05.*
[01:08:28 URL](ruclips.net/video/PbguWyuZ4Ts/видео.html) *🤔 In the fourth step, defining the decision rule is crucial. For a two-tailed test with an alpha of 0.05, the critical values are ±1.96 for a Z-test.*
[01:10:50 URL](ruclips.net/video/PbguWyuZ4Ts/видео.html) *🧮 Calculating the Z-test statistics involves the formula (X - μ) / (σ / √n), where σ is the standard deviation. Always use root n for sample data to account for the standard error.*
[01:16:39 URL](ruclips.net/video/PbguWyuZ4Ts/видео.html) *🚫 If the calculated Z value falls outside the range of -1.96 to +1.96 based on the decision rule, reject the null hypothesis.*
[01:28:29 URL](ruclips.net/video/PbguWyuZ4Ts/видео.html) *🧠 Applying the learned concepts to a practical problem, rejection of the null hypothesis indicates an increase in intelligence in this scenario.*
thank you
Bank - ATM Opening in Given location -- 1. Collect the Population Mean - Personas Visiting the ATM - 2. Select the Sample Mean - typical > 30 sample persons vesting particular ATM 3. From the Population , we can find the std deviation 4. Based on the alpha - we can find the confidence intervals 5. calculate Z- Value 6. Based on the z -value - we can accept/reject the Null hypothesis
I completely agree to you and I was thinking the same way as you but I got stuck at the situation that what would be the null and alternate hypothesis in this case? Can you help me by answering that?
Data science feels so exciting when you teach Krish!
After watching all statistics 7-day live sessions and now I have got so much confidence in stats, and I am very sure that I could learn data science subject. The way you teach is very clear and absolutely very easy to understand which I didn't find in my university. A Big Thanks to you Krish Naik ji for your great effort to help me and many students with your knowledge to shine their future and it is much appreciated. 🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏🙏
how are you preparing for data science
first i was doing petroleum engineering and one day I cam on this channel now I am preparing for data analyst/scientist job. :)
19:00 1 tail and 2 tail test
49:00 t test- when to use
54:49 degree of freedom
1:04:08 Problem
Best video explaining p values and clear explanation of one and two tailed tests. Great work.
Thanks for the video lectures. You have very clearly explained the concepts from basic to advanced level. 👍. Thanks for all your efforts. Much appreciated
06:34 📊 Type 1 error occurs when we reject the null hypothesis when it's actually true, leading to a false positive conclusion.
14:28 📉 Type 2 error happens when we fail to reject the null hypothesis when it's actually false, resulting in a false negative conclusion.
19:04 📝 In a hypothesis test, a one-tailed test focuses on one direction (e.g., greater than or less than), while a two-tailed test examines both directions.
26:15 🧠 Understanding the context of a question is crucial to determine whether a hypothesis test is one-tailed or two-tailed.
32:41 📏 Point estimate in inferential statistics involves using sample data to estimate population parameters, recognizing that the estimate may not be exact but approximate.
33:01 📊 Point estimates in statistics provide values that estimate parameters, such as the mean, from a sample.
33:30 🛠 Confidence intervals are used to estimate the population mean, typically calculated as point estimate plus or minus the margin of error.
35:56 📈 When given the population standard deviation, the z-test formula is applied to calculate the confidence interval.
37:01 🧮 Given population standard deviation, sample size, and confidence level, constructing a confidence interval involves calculating the margin of error using the z-test.
51:23 🧪 When population standard deviation is unknown, the t-test is used to calculate confidence intervals, with the sample standard deviation and sample size.
01:09:26 📊 Z-test helps determine if a value falls within a specific range by comparing it with critical values like 1.96 for 95% confidence.
01:11:05 📈 Z-test formula: (x - μ) / (σ / √n). Standard error (σ / √n) becomes relevant for larger sample sizes to match population means.
01:15:06 🧮 Test statistic computation for Z-test: x̄ - μ / (σ / √n), considering sample mean, population mean, and sample size.
01:16:53 🧠 Rejecting the null hypothesis in hypothesis testing suggests an effect, like increased intelligence due to medication.
01:26:41 📉 T-test formula for test statistic: (x̄ - μ) / (s / √n), with x̄ as sample mean, μ as population mean, s as sample standard deviation, and n as sample size.
01:28:29 🎓 Rejecting the null hypothesis indicates an increase in intelligence, highlighting the importance of hypothesis testing in real-world scenarios.
🎯 Key Takeaways for quick navigation:
20:25 📊 Two-Tailed Test: Checks if the rate is different, greater, or less.
29:19 🎯 Confidence Interval: Uses point estimate, a statistic, to estimate a parameter within a range.
30:19 📊 Confidence Interval Basics: Calculations involve finding upper and lower bounds to estimate parameters.
32:27 📊 Point estimate is the value of any statistic estimating a parameter, like the mean.
33:30 📏 Confidence interval: formula - point estimate ± margin of error, providing a range around the mean.
35:13 🎯 Given a problem statement, population standard deviation, sample size, alpha, and mean, construct a confidence interval using the Z-test formula.
50:19 🔄 If population standard deviation is not given, use a T-test for the confidence interval formula, even if the sample size is less than 30.
01:08:28 📊 Specify decision rule in hypothesis testing considering alpha and tail type.
01:09:06 🧪 Two-tailed test with alpha 0.05: critical values are ±1.96 for a standard normal distribution.
01:11:19 📈 Standard error in z-test formula: divide by the square root of the sample size (root n).
01:14:11 📝 Z-test formula for test statistics: (x - μ) / (standard deviation / root n).
01:16:26 🤔 Rejecting the null hypothesis in a z-test implies the medication had an impact; direction depends on the z-score sign.
Made with HARPA AI
Really appreciated for your dedication and effort. Thank you @Krish Naik Sir.
too good to learn with ease by the help of your simple and clean explanation...thank you
After watching this playlist till now. I couldn't understand why my college teacher does not teach me like this. If my college teacher teaches me like you sir I will never bunk the classes.
Thats why They are college teacher, And Krish is a data scientist. But I didnt mean to demean a teacher.
HI Krish,
This live sessions has been the most useful videos i have found in RUclips. i had viewed lot of other channels, but you the way you explain with huge patience. much appreciated. Thank you for providing this for free. thank you a ton, and you are like andrew ng for me.
thanks Krish concepts are very clear now...
Data science is easy when krish nail sir is teaching 💯❤
Thank you sir your way of teaching is fantastic 😍
The way you taught stats, make me fall in love with this.
GuruJi Pranam
thank you
Thank you for such effort sir!!
really learn a lot
Sir I have request for Full Crash Course for Machine Learning that only include theory in one part and other one should be for coding problems
#KingKrish
FP is type 1 error and FN is type 2 error
I agree.
Hi Krish Sir, I think you have entered the confusion matrix incorrectly. And false negative is type 2 error.
++
If anybody know answer of this question so please help me 🙏
1.what is use of confidence interval ?
2.population standard deviation is given so we use z test otherwise t test, is that right
@@jemilmangukiya8134 for 1st question confidence interval is used for how "good" an estimate is and for 2nd question yeah if u don't have population standard deviation we go for T test
basically first we have to decide which is actual side and which is predicted one .. let's say vertical side actual and horizontal is predicted then .. 1st block will be TN , second block in horizontal will be FP , next line 1st block FN and 2nd block TP ..
1 0
1
0
only 2 videos left, you are the best krish :3
Hello Krish., today I saw you video really amazing.realy the best to understand and learn for beginners. And thank you soo much for providing such a great resources... I woked in different sector and have gap. Now I am mother of twin babies. Have a long gap .I don't have time. Still making myself free to study.... But still following your videos and practicing.. Am I eligible to get job in 35+..???can you guide me.. How to follow you and how to practice in better way.. ?? Hope you will replay me...
8:37,i THINK Null hypothesis can never be true or accept ..we either reject Null hypothesis or fail to reject
finished watching
I have a doubt. First we used 1.96 in confidence interval formula, and then got the value of confidence interval but in the later part of video during hypothesis testing(z test) we directly used 1.96 as confidence interval without substituting in formula. Which way of calculating confidence interval is right?
1st way is for calculating "CONFIDENCE INTERVAL". The second way is for deciding H0 and H1.
Till day 4 it's understandable for non maths or science background students but after dy 4 m totally confuse u can check your comment section by counting the dy 1 comments nd now on day 4 it's decreasing that means only those students are with u till now who are already knows the basic or intermediate level of stats i thought this video series will help me but I not found usefull please help
Hi Krish, please also upload day 4 and 5 sessions along with notes
95% is Confidence level not confidence interval you are doing good job
no . It is confidence interval not confidence level
@@ankitbiswas8380 please google it its or read basic book
@@shrikantdeshmukh7951 googled by dumb and dumber and your photo popped up .
Sir covarience corelation wagera kab padhenge?
Please add table of contents in the description
i think False negative is the type 2 error , not False True negative
medication affect the intelligence, how it is shown by the null hypothesis as H0= u=100?can anyone explain me this?
1:09:50,why to subtract from 1
IQ increase by 100 percent is true
Sir when will Be the new fsds course is coming...please make a new summer batch sir nearby april or may ...😭😭 Some of the people missed chance this time
Timing 33.20 you are saying sample mean can be greater than population mean how can that be then sample size has to be greater than population size which can't be true....please explain
Not sample size....sir is saying mean of sample may be greater than mean of population
@@The_artt_factoryy ok understood.... thanks
I think the confusion matrix that you showed in this video is wrong.
these playlist is also for data analyst or only for data scientist
sir we took (xi-mean)/std in 2nd day and here you are taking (xbar - mean)/ std ??????? please clear my confusion
Can you please upload the notes ?
SIR when will the new batch start for full stack data science course by ineuron
I have the same question
Hi,
In the part 1:00:20 of the video, the curve drawn is according to the 2 tail test. So shouldn't we take the value for t0.025 from the 2 tails in t table i.e. 2.492
Hey Krish, is that z-test and z-score are same? as per your statement 40.32 ..i don't think so?
Type 1 and Type2 errors: suppose, we think about hiring process, any student selected in company with support of relatives but actually he is no able to work on that position, even he not have any skills-- Type 1 error. In case of other student , he is not selected in company but he has all skills realated to any position-Type 2 error.
Doubt: At 1:10:36 you only took Z(alpha/2) and did not multiply it with (sigma/underoot n). So Z(alpha/2) value is common for all problems/cases/scenarios?
18:00
when solving last problem for 't test' in live, why did he take mean of population (mu) = 100.
we are going for 't test' as we don't have (mu). can anyone answer if understood?
Why is i alpha divided by 2 in that CI formula?
can someone tell me how do we conclude as medication has improved on rejecting null hypothesis? Is it because it falls > 2.05 region? how do we know null hypothesis states that it is showing the values for medication not improved on sample?
Because of of the pop mean we constructed the CI which in our case is 100.So this is considered as true.But after one sample z test we find that it is wrong since our obtained value falls outside the interval.Since the movement is to +ve side,we conclude medication has improved it
In confusion Matrix,
First row: TP FN
2nd. row: FP TN
FP is type -I error
FN is type -II error
How FN can be type 2 error?
F basically means rejecting the Ho
And N means Ho is in fact not True.
I think TN will be the type 2 error!
What m said is right.
@@akashgautam1909 type 1 error meaning - we reject the H0 but in reality it is true
type 2 error meaning - we accept the H0 but in reality it is false
so let's take an example
Our H0 = person is innocent
but the court found him guilty, but in reality, he is innocent
so this is a type 1 error because our H0 == reality
but the court rejected it meaning, FALSE POSITIVE (something is false but we say it is true)
in next case
H0 = person is innocent
and the court found him innocent, but in reality, he is guilty.
so this is a type 2 error because our H0 != reality but still court accepted it
This means its FALSE NEGATIVE (meaning, something is true but we say it is false)
In this case, he is guilty but we say he is innocent
already you are dividing alpha/2 where alpha is 0.05 we get 0.025 in each tail why are you subtracting 1-0.025 for one tail , you can take 0.025 directly as it is for 1 tail. did you take that as we dont have values to check for 0.025 in z table? please respond
This live is not that good when compared with the previous 4 live sessions on stats
Don't know sir
how 3.65 is rejected it is greater than the pvalue right 3.65>1.96 right can somebody explain
See as 14.60>1.96 we concluded that it is beyond Confidence Interval thus rejecting the null hypothesis, similarly this time its 3.65>1.96 so we again will reject the null hypothesis.
The question here is has the medication(in case where sample mean is 110) improved IQ or has it decreased IQ!?
-Do let me know if you got the answer for this part, I hope you understood the above section to why we are rejecting null hypothesis( which is mu=100)
liked
Sir can you please share me these materials?
why dont u revert on mail??
Can anybody let me know where I can find all these notes, please?
Sir notes toh provide krwadete Jo bhi aap likhrhe ho
yes my IQ increased
Why the volume is so low?
❤️❤️❤️🙏🙏
Outcome 2 should be Type 2 error and Outcome 3 should be Type 1 error isn't it ?
as Type 1 => we accept the null hypothesis, in reality it is false i.e. FP and Vice versa for Type 2 (FN)
Please correct me, if I am wrong.
You are correct , you think of it as a severity as well , Type 2 (False Negative , escape from punishment ) , Type 1 (False Positive, punished inspite being not guilty ).
FP - Type 1 ; FN - Type 2 ..... i thinl there was a mistake when confusion matrix was drawn
Why not take 36 instead of 25 to simplify things for everyone😂
The content is basic statistics. The title is misleading.
Please tell us what is advance the
@@krishnaik06 Hey Krish
What do we have to find out in ATM question? CI?
can u tell what is advanced then for jobs and placements if u are pointing out a problem u should also suggest solution
my IQ increased
Sir 2 years se to abi tk aap statistics cover nhi kr paye or Hindi me other further topics pr video bnane ki bat kr rhe h that not good na sir 🥲🥲
ruclips.net/video/2VE6AZPS6bI/видео.html
One more excellent explanation
On camera you are taking snacks does not look good. Please either abstain from doing this or stop your video. Otherwise your teaching is excellent.
Thank you for your efforts ❤