p-Value (Statistics made simple)
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- Опубликовано: 21 авг 2024
- What is the p-Value in statistics?
The p-value is one of the most important quantities in statistics for interpreting hypothesis tests.
But what does the p-value mean?
The p-value is the probability of the observed result, plus even more extreme results if the null hypothesis is true.
For the calculation of the p-value a suitable hypothesis test must first be found. If the suitable hypothesis test is found you can calculate the p-value in the statistic calculator on DATAtab. The best known hypothesis tests are:
datatab.net/st...
More Information about the p-Value:
datatab.net/tu...
If you like, please find our e-Book here: datatab.net/statistics-book 😎
Hello : I would just like to ask about the book if it's available in printed book or only in pdf ?
and how about méthods of calculation ( p value as an example ), are they explaining the formula in the book and or only how to use Statistics Software ?
Thanks in advance ! :)
@@mohamedaitguennoun6204 Hi, thanks for your question! It is only available in pdf form and we explain the formulars needed for the different tests! Thanks and Regards Mathias
@datatab I bought the book but was unable to download the pdf. can you please help me with that?
Got an email and could access pdf from there. Thanks
The clearest and simple explanation of P value I've ever seen. Thank you!
The first video which really explains p-value, thank you!
Glad it was helpful!
No kidding. The best explanation on p-value of all.
Thanks!!!
I hate these videos that says "made simple" or "for dummies" and actually they make it more confusing to understand. I am more confused after watching the video than I was before.
iiuc: p stands for a probability of a sample differing from the null-hypothesis of your whole population. alpha value on the other hand shows if your population data is reliable. OP plz correct me if I'm wrong.
@@VollspechtThe P value is the probability that the difference between the sample and the population was due to chance. The bigger the difference, the less likely it was due to chance, and the more likely there is something causing the difference.
If you wake up and all four of your tires are popped, are you going to think it just happened by chance or are you going to think someone came and did it?
The weirder the circumstances, the less they are likely to be due to random occurrence. AKA, the lower the P value, the weirder the occurrence, or the more “off” something is from what it normally should be, and thus, the less likely it’s due to chance. I hope this makes sense.
Can you ask a specific question? She seemed to explain it pretty simply and clearly to me
Explanation of p-Value is clear. Other explanations are wholly incomplete!
Idk why u get so much backlash it helped me sum the subject up. Very nice video.
I understand it. Tell me if am wrong.
Basically, imagine if I said that not everyone who gets slapped is hurt - this is the null hypothesis. You would argue that's silly - you believe being slapped means you are hurt - alternative hypothesis.
We conduct a test and we find out less than 5%(p value) of people who are slapped are not hurt. You can reject my hypothesis because if I was right we would expect more people not to get hurt.
If more than 5% of people slapped are not hurt, then you accept my hypothesis (null) or doubt yours because I said that there is no relationship and the new data causes you to doubt.
Please tell me what you think. Am open to criticism
In this example, once you get the p-value of 0.03, whether you are going to reject or accept the null hypothesis. For alpha of 0.05, this p-value will be rejected, and the conclusion is that there is a difference in the salaries. Right?
Yes null hypothesis is rejected
If P is low null must go to reject
Then what's the point of saying that probability of the difference is salary being 250 euros is 3% given that the null is true, if you are rejecting the null in the first place at 5% level of significance?
@@abhijaynair1783 she made a mistake on that. it should reject the null so it means there is statically difference in salary in population.
You are correct
@@drumsinthehouse8116
why we reject null hypothesis if p
p value is the probability/possibility that a certain observed difference of sampled data, eg mean, from population could be due to random sampling error without actual difference, which is just as a result of the nature of the data like scatter, or sample size. P value is calculated for every difference of mean. It is dynamic. Not fixed.
This is the best simplified explanation of p-value I have come across!! Thank you!! 😊
Glad it was helpful!
P value of .005 values that use sample sizes for human rhythms. Human Circadians. Titrations Peaks and troughs for best practices.
Thanks for this.
I am still confused about the difference between p and α, though. You start describing α as though it is something we freely choose, but at 3:53 you speak as though it is something we calculate and then assess to see how significant it is.
You calculate the p-value. The α level is something you decide, but not freely, a lower value means it will be more difficult to reject the null hypothesis. Maybe that part where she says alpha
alpha is just the threshold, you can chose it to be any number you want, however keep in mind that the bigger the alpha, the more you are allowing false positives to occur
alpha of 0.01 is - I am ok with a false positive 1 in 100 times
alpha of 0.5 is - i am ok with half of the predictions being wrong
I can tell English is not your first language but you have mastered this explanation perfectly. Thank you maam
She's most likely german so her english is perfect from childhood
Very clear explanation!
Nice explanation, but the populations were mixed from the start
Thanks, this is better than Minitab!
Thank you for the feedback! And a lot cheaper : ) Would you like a one month trial version?
@@datatab is this valid now?
Mam, why we reject null hypothesis if p
it actually means that there is a 3% only to see that same result if the null hypothesis is true and so there is no difference. If the p is high you have big possibilities to 'get' that result under the null hypothesis (in the case that there is no difference) so you want a low p-value (= meaning that there are no many chances that you would get that statistic result if actually there was no difference between the values) and sure lower than the a=0.05 significance level. The significance level is low for making sure that you have strong evidence against the null hypothesis and we dont just find differences everywhere. I hope this helps you! its very confusing ik
Very interesting, thank you
Glad you enjoyed it
very helpful comment!!!💯. I like how simplified your explanations are., thanks!1 👍👍👍👍👍👍
Many thanks : )
thx...an ad for data tab ....
: )
Excellent explanation !!!!
Do you pay it??
Can you please make videos on statistics that are needed in economics
Hello Sabrina, yes why not! For which topics?
he data sets I am comparing have different data sets within them e.g. one set has 10 values, the other has 15 values and another having 22, how can I compare this data to find the p-value, as one way anova requires you to have the same number of data in each set
lifesaver 🙏🏼
Thanks!
Very very good explanation
Many thanks!
Convoluted video, not true. Simply put, P-Value is the percentage of Luck and False positives affecting your results instead of your experimented factors. So in an even more simpler way: P-Value % = Luck, the less % the less luck and more real effect of factors experimented by you.
@@ASMM1981EGY Your explanation is much more meaningful and understandable then this whole confusing video !
*Thank you very much 🌻🙏*
You’re welcome 😊
clever girl. thank you
Thanks.
You're welcome
thank you so much
You're welcome!
Thank you
By difference in the sample, do you mean educational/experiance level?
Where in the video is that? Regards Hannah
thanks
So p is saying that it’s only 3% *likely due to chance* that men and women have different salaries?
Does that mean it’s 97% *likely due to an existing effect* , like discrimination?
Who is from the Data Science Toolbox?
Woh!!!!
Thanks!
What a waste promoting a product instead of explaining the concept.
That's a joke, right? First of all we are on youtube where everything is full of ads and secondly the first 5:15 are explaining the concept and just the about one minute at the end is about DATAtab.
This is not statistics but Datatab lectures. No one can learn statistics with these tools. You need math to explain this, otherwise it makes no sense, it is nonsense.
Huh?
Their calculator doesn't work. Don't waste your time.
just a video to promote their website, didn't like it
Poor explanation ! Highly confusing.
Such a confusing video
Terribly explained. Consider deleting the channel please.