Uploaded May 2014 | Updated September 2026, 2 hours ago
I work through an example of a confidence interval and a hypothesis test for the difference in population proportions (based on the normal approximation).
The example involves an experiment investigating a possible effect of magnetic pulse on the ability of homing pigeons to navigate. The data used in this video is taken from:
Holland et al. (2013). A magnetic pulse does not affect homing pigeon navigation: a GPS tracking experiment. The Journal of Experimental Biology, 216: 2192-2200.
See the article for further details on the experiment.
If you want more information about these procedures, I have a video introduction to inference procedures to two proportions available at: youtu.be/g0at6LpYvHc.
I work through an example of a confidence interval and a hypothesis test for the difference in population proportions (based on the normal approximation).
The example involves an experiment investigating a possible effect of magnetic pulse on the ability of homing pigeons to navigate. The data used in this video is taken from:
Holland et al. (2013). A magnetic pulse does not affect homing pigeon navigation: a GPS tracking experiment. The Journal of Experimental Biology, 216: 2192-2200.
See the article for further details on the experiment.
If you want more information about these procedures, I have a video introduction to inference procedures to two proportions available at: youtu.be/g0at6LpYvHc.






![Introduction to the Central Limit Theorem
I discuss the central limit theorem, a very important concept in the world of statistics. I illustrate the concept by sampling from two different distributions, and for both distributions plot the sampling distribution of the sample mean for various sample sizes. I also discuss why the central limit theorem is important in statistics, and work through a probability calculation. (For the most part this is a non-technical treatment, and simply illustrates the important implications of the central limit theorem.)
For those using R, here is the R code to find the probability for the example in this video:
Finding the (approximate) probability that the mean salary of 100 randomly selected employees exceeds $66,000:
1-pnorm(66000,62000,32000/sqrt(100))
[1] 0.1056498
Or, standardizing:
1-pnorm((66000-62000)/(32000/sqrt(100)))
[1] 0.1056498
1-pnorm(1.25)
[1] 0.1056498 Introduction to the Central Limit Theorem](https://i.ytimg.com/vi/Pujol1yC1_A/mqdefault.jpg)



