Seminar 2 Flashcards
What is a confidence interval?
Confidence intervals show us the range of sample means we would expect to have if we collected samples of the same size ad infinitum. We can make the assumption that the population mean is somewhere in that range.
How do confidence intervals help us infer differences in the means of measurements?
If we have two confidence intervals next to each other, we can check if there are significant differences between the two measurements simply by looking at whether the intervals overlap.
What is the confidence level? How does it relate to confidence intervals?
Confidence level is related to the amount of error we are willing to accept when calculating the range of the population mean. We can’t make them too high or we can get a type of error.
What happens to the confidence interval when we increase the confidence level?
It increases in size
What is the margin of error?
It’s half of the confidence interval (the top and bottom half from the point estimate)
What is the point estimate?
The sample mean
What do I mean by DV ~ IV?
Is there an effect of the independent variable, on the dependent variable? If the independent variable is drug, and the dependent variable is blood sugar, then:
Blood_sugar ~ Drug
What is the normal distribution?
It’s a bell-shaped distribution that often appears when we collect and plot data. Things like heights and weights, for example, tend to follow the normal distribution.
You collect 500 people’s heights and find that (a) your data follows the normal distribution, and (b) the mean height is 170.6cm. What is the median and mode?
In the normal distribution, the mean = median = mode. Therefore, the median and mode are 170.6cm.
What happens to the t-distribution as you increase the degrees of freedom?
It gets thinner (more leptokurtic)
How many observations do we expect to have one SD away from the mean in normally distributed data?
Around 68% of all observations sit one SD away from the mean.
What does asking whether there is an effect of IV on VD, what does it actually mean?
Whether there is a significant difference in the means between A and B.
Sally wants to check whether there is a significant difference in the mean time senior HR employees spend browsing the Internet, compared to junior HR employees, during work hours. If we assume that IT logs this information and hands it over to Sally, given everything you’ve learned so far, can you describe a way for Sally to use this data in order to understand the differences?
This is classic hypothesis testing.
H0: There is no effect of seniority on time spent browsing
H1: There is an effect of seniority on time spent browsing.
