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Sample notation: When to use capital $N$ vs lowercase $n$? In statistics and psychological research, what is denoted by capital N N vs lowercase n n? I work in psychological research and I've seen them used in two ways: Capital N N represents the entirety of our sample and lowercase n n are the groups in that sample e g We run an RCT with 100 100 participants, 50 50 in control and 50 50 in treatment, so N = 100 N = 100 and n = 50 n = 50 in each
Hypothesis testing: Fishers exact test and Binomial test Considering the population of girls with tastes disorders, I do a binomial test with number of success k = 7, number of trials n = 8, and probability of success p = 0 5, to test my null hypothesis H0 = "my cake tastes good for no more than 50% of the population of girls with taste disorders" In python I can run binomtest(7, 8, 0 5, alternative="greater") which gives the following result
what is the difference between a two-sample t-test and a paired t-test When you use a paired T-test, you are essentially doing a one-sample test, where your one sample consists of the paired differences between outcomes in two groups If you create a new sample of these difference values and then apply the formula for a one-sample T-test, you will see that this is equivalent to the paired test
Ideal BandPass Filter - Signal Processing Stack Exchange Let suppose x (t)= ∑ k=−∞∞ R(t − kT) ∑ k = − ∞ ∞ R (t − k T) R(t) = {1 0 [0, 2T] otherwise R (t) = {1 [0, 2 T] 0 otherwise x (t) is the input to an ideal bandpass filter with BandWidth = 1 (2T) BandWidth = 1 (2 T) and Center Frequency = L (T) Center Frequency = L (T) How can i find the output y (t) any help will be appreciated
Building a linear model for a ratio vs. percentage? Suppose I want to build a model to predict some kind of ratio or percentage For example, let's say I want to predict the number of boys vs girls who will attend a party, and features of the party
Interpretation of Shapiro-Wilk test - Cross Validated Considering that you are pretty new to statistics, I suspect that you are thinking about this because these are residuals of an estimate of a mean and you want to know whether the assumption of normality is valid for confidence estimates using a t t -distribution t t -tests are quite robust to violations of this assumption, the data look vaguely normal in Henry's q-q plot, and the Shapiro
terminology - Extrapolation v. Interpolation - Cross Validated What is the difference between extrapolation and interpolation, and what is the most precise way of using these terms? For example, I have seen a statement in a paper using interpolation as: "The
How to measure the shift between two cumulative distribution functions . . . If we look at the CDF as whole (green always above red) this suggests in your sample population, girls score higher than boys Whether or not this result is statistically significant is yet to be determined How meaningful is the difference overall? (edited as a response to @whuber) : This depends on how you use it
Correlation and standard deviation - Cross Validated The formula for the correlation is equal to the covariance between x and y, divided by the standard deviations of x and y So what happens if you keep on denominator (where the standard deviation is) larger and large?