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Statistical Significance

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Uploaded by on Feb 4, 2008

Statistical Significance and the probability of the null hypothesis.

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Education

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Uploader Comments (headlessprofessor)

  • where is that cut off point?

    why .05?

  • The .05 level is generally accepted (by most academic journals in the behavioral sciences). Large sample epidemiological studies and pharmacology experiments may prefer the .01 level. Preliminary and pilot studies with small samples might use a .1 level.

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  • Very good video. It helped me clear up a lot of confusion about p value and null hypothesis. Thank you professor

  • It´s like freaking Denzel Washington is explaining me statistics! XD

  • @headlessprofessor I disagree with the 0.05 part. A P-Value of 0.05 is evidence, but a marginal one. To me A P-value of 0.05 is based on chance. I think a Type I error can occurred if I dismiss it at P=0.05, which means there is a 5% chance of the null-hypothesis being true. I will dismiss the null-hypothesis if P<0.05.

  • thanks for clearing that up for me, my uni lecturers have not managed it after a year but you did it in 6mins 45.

  • The p-value is NOT the probability of the null hypothesis after seeing the data. It is the probability, assuming that the null hypothesis is true, of getting data equal to or more extreme than the data actually observed. If D is the observed data and H is the null hypothesis it's p(D|H) not p(H|D). In general these are very different. (Example: the probability that a man is dead given that he was hanged is different to the probability that he was hanged given that he is dead!)

  • very clear teaching. and thank you for reminding me to cut my finger nails. lol.

  • Thank you so much! this was awesome!

    I'm a 3rd year psych student who has pretty much gotten distinctions every year, but every year I have become dangerously close to failing stats (52/51 and now 3rd year i'm struggling again)

    I can never understand stats but this was truly helpful!

  • This is no true random variation. Chance is merely an illusion.

  • An effect can chronologically precede a cause. Retrocausality.

  • Thanks for excellent video

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