Of all the videos on interpreting an independent two-sample T-test, this one is the best on UA-cam. Clear, concise and well delivered. Thank you so much :)
Thank you. I appreciate these short, to-the-point, videos. Often, these types of videos are too long and wordy, so we really appreciate these concise versions!
Great video! It may be more correct to say that there is a 95% probability that the confidence interval you calculated contains the true population mean though. Keep making great vids!
This is an awesome vid. I can follow all you explanations from start up until the first 4.00 mins, but I quite can't follow the remaining. Especially, where do the lower and upper scores come from? Please kindly explain.
If my equal variances assumed Sig is equal to 0.000, should I take into acount the further results, or does that mean that it's not significant at all? Because My Sig for the t-test is equal to 0.003, so it should be significant?
you did not explain the t value? whats the purpose of showing t value? secondly plz tell how this table values can be tabulated in research work ? can we copy this table as it is? plz must reply. thank you its very helpful
PLZ RESPOND URGENTLY. Hey Mr. Varshney, can you plz provide me yr email? I'm now getting more confused after reading the comments? I'll then inbox you. Thanks
Usually, 0.05 is used most of the time, especially if you are not researching something completely new. If you come up with a completely new theory and need to test it, and there are no other studies on it then you should probably go for 0.01 level of significance. I'm not entirely sure but this is what I remember from A level psychology haha
hye. ok. the p value is 0.405 which is bigger than 0.05. so it is not significant. if not significant, we reject the null hypothesis right? im confused.
+Veena M no actually, we must say "we do not reject the null hypothesis" if pvalue greater, else if pvalue smaller then we must say "we reject the null hypothesis and accept the alternative hypothesis"
When the significance (sig) in the Leverne's Test column is greater than .05, you use equal variances, and when it's less than .05, you use not assumed.
The sig .604 is for the Laverne's Test (which gives you direction as to whether you use the "equal variances assumed" or "equal variances not assumed" row), and the sig .405 is for the actual T-test within the "equal variances assumed" row. Laverne's Test sig tells you which of those two rows to use, and the t-test sig tells you whether or not to reject the null hypothesis.
You are totally correct, but by guiding viewers saying "accepting the null hypothesis" makes it so much easier to understand how to "fail to reject it". The latter example is inbued with two negations in one sentence, having me a bit confused when trying to sort stuff out. I think I will remember this for ever now.
@1:14 * Pretty sure you hypothesis should be a little something more like this, Let male = the true mean male competency score before starting their job. Let female = the true mean female competency score before starting their job. H0: male - female = 0 H(either 1or A): male - female 0 Let = 0.05
That first sig of .527 (from the Laverne's Test) would tell you to use the "equal variances assumed" row, and then the .021 (from the t-test in the "equal variances assumed" row) would direct you to reject the null hypothesis. Deeper explanation: Laverne's Test creates is own null hypothesis aside from the "regular" one. Laverne's null hypothesis is that the variances for both valuables are the same. So, if the sig for Laverne is >.05, we would accept the null hypothesis that the variances are the same, which would direct us to use the "equal variances assumed" row. Once we get to that row, we slide down to the other sig, the one for the t-test. The null hypothesis of the t-test is that the means of both variables are the same. So, if the sig here is
Of all the videos on interpreting an independent two-sample T-test, this one is the best on UA-cam. Clear, concise and well delivered. Thank you so much :)
Just wanted to say you are incredibly awesome for making this video! Over a decade later and you're still impacting others. Thank you so much!
Thank you. I appreciate these short, to-the-point, videos. Often, these types of videos are too long and wordy, so we really appreciate these concise versions!
prob the best stats tutorial out here.
Thank heavens I found you, I was despairing of finishing a quantitative analysis assigment, but now I'm finally understanding...
This video is amazing it stopped me from throwing my laptop at my teacher mid class
Omg, you explained it way better than my professor. After watching his lecture I was confused and had to check UA-cam university. Thank you so much
2nd Year Psych Student here!
Great explanation thank you!
Thank you so much, Madam! You have cleared the confusion I had about independent samples t-test
I have watched some video about this, and I finally get to understand through your video. Thanks!
thanks for simplifying the analysis. A street educated data analysis I can say most of struggle with storytelling.
THANK YOU SO MUCH you really saved me from failing my exam
Great video! It may be more correct to say that there is a 95% probability that the confidence interval you calculated contains the true population mean though.
Keep making great vids!
I just want to say that your videos are awesome! Thank you for uploading :)
Thank You very much , your videos are great help to those struggling with their thesis statistics :D
Exactly, just to the point, and simple.
Very clear and simple way to teach. Thank you very much sir!
this was the shortes video on tt test but the most clear ffs thank you
GREAT explanation, thank you so much!
Clear, concise, straight. Thank you
This is an awesome vid. I can follow all you explanations from start up until the first 4.00 mins, but I quite can't follow the remaining. Especially, where do the lower and upper scores come from? Please kindly explain.
I need more of this videos please
really great video. No drama.
Its really helpfull thanks dear
i could cry. thank u
Thank you! What a great tutorial.. nice voice too
you are so cool! thanks and keep doing videos; nice voice.
Pretty sure this video just saved my paper XD
Mine too.
thanks so much you're a life saver!!!!
Thank you so much for this!
very helpful refresher, thank you
Do you have a video for how to read the output if equal variances are NOT assumed?
Excellent explanation - thank you
Extremely helpful! Thanks!
Many thanks!
what if the size of the two groups to be compared is extremely different? (ex. group 1: n=200, group 2: n=60)
This helped a lot. Thanks.
great explanation - thank you so much!
very helpful, thank you!
hy,hhow to interpret and what do you mean the mean and std deviation from the t test?
Which significance value we mention in interpretation
Sig or sig two tailed
I love your voice
thanks! this is helpful!
well explained, thanks a lot for the help.
what if there is 2 test variables? do we still need a hypothesis for each variable??
So the "Sig (2-tailed)" is your p value?
The answer should be "fail to reject the null hypothesis"- this is misleading and confusing info...
I think the way you put it is more confusing tbh..
Not always the null hypothesis is failed
Thank you queen!
Q. Your sample sizes are unequal, how can you read "equal variances assumed"?
Thank you so much.
If my equal variances assumed Sig is equal to 0.000, should I take into acount the further results, or does that mean that it's not significant at all? Because My Sig for the t-test is equal to 0.003, so it should be significant?
So where would you find the denominator of t-obtained?
you did not explain the t value? whats the purpose of showing t value? secondly plz tell how this table values can be tabulated in research work ? can we copy this table as it is? plz must reply. thank you its very helpful
WE NEVER ACCEPT THE NULL.
WE JUST REJECT OR FAIL TO REJECT, WHICH MEANS, THAT WE DON'T KNOW!!!
How do i get the analyze video
Good elaboration
You should not accept the null hypothesis, rather, "you fail to reject it"
How do I identify what my initial significance value and significance level should be?
you do not accept the null hypothesis. you simply DON'T REJECT it. Model is insignificant to tell the dependancy!
very close, but you "fail to reject the null hypothesis"
PLZ RESPOND URGENTLY.
Hey Mr. Varshney, can you plz provide me yr email? I'm now getting more confused after reading the comments? I'll then inbox you. Thanks
hi, sorry dumb question but how to you choose your significance level?
Usually, 0.05 is used most of the time, especially if you are not researching something completely new. If you come up with a completely new theory and need to test it, and there are no other studies on it then you should probably go for 0.01 level of significance. I'm not entirely sure but this is what I remember from A level psychology haha
hye. ok. the p value is 0.405 which is bigger than 0.05. so it is not significant. if not significant, we reject the null hypothesis right? im confused.
+Amanina Zawani yeah me too.
If p value is greater than 0.05, accept the null hypothesis, if not, reject :/ think so
+Veena M no actually, we must say "we do not reject the null hypothesis" if pvalue greater, else if pvalue smaller then we must say "we reject the null hypothesis and accept the alternative hypothesis"
TYSMMMM
how do i download this video cause its been helpful
Thank you so much !!!
How, do you decide when to either use the p value for equal variances assumed or equal variances not assumed
When the significance (sig) in the Leverne's Test column is greater than .05, you use equal variances, and when it's less than .05, you use not assumed.
what do you do if the sig (pvalue) for the t-test is p
Reject the null hypothesis
What is the difference between the Sig. of .604 and the Sig. 2-tailed of .405 ? Thanks
The sig .604 is for the Laverne's Test (which gives you direction as to whether you use the "equal variances assumed" or "equal variances not assumed" row), and the sig .405 is for the actual T-test within the "equal variances assumed" row. Laverne's Test sig tells you which of those two rows to use, and the t-test sig tells you whether or not to reject the null hypothesis.
God bless your soul
thank you
ermm...one never "accepts" null hypothesis but "fails to reject" it.
You are totally correct, but by guiding viewers saying "accepting the null hypothesis" makes it so much easier to understand how to "fail to reject it". The latter example is inbued with two negations in one sentence, having me a bit confused when trying to sort stuff out. I think I will remember this for ever now.
At my uni we are taught “accept”. I’m first year so this might be an entry level way of putting it, doesn’t mean it’s wrong.
@1:14
* Pretty sure you hypothesis should be a little something more like this,
Let male = the true mean male competency score before starting their job.
Let female = the true mean female competency score before starting their job.
H0: male - female = 0
H(either 1or A): male - female 0
Let = 0.05
not clear
THX FOR NOTHING
Then thanks Beyonce
So what happens if your Sig. is for example .527 but you Sig. (2-tailed) is .021 and .024 with alpha=.05?
That first sig of .527 (from the Laverne's Test) would tell you to use the "equal variances assumed" row, and then the .021 (from the t-test in the "equal variances assumed" row) would direct you to reject the null hypothesis.
Deeper explanation:
Laverne's Test creates is own null hypothesis aside from the "regular" one. Laverne's null hypothesis is that the variances for both valuables are the same. So, if the sig for Laverne is >.05, we would accept the null hypothesis that the variances are the same, which would direct us to use the "equal variances assumed" row.
Once we get to that row, we slide down to the other sig, the one for the t-test. The null hypothesis of the t-test is that the means of both variables are the same. So, if the sig here is
Thank you so much.