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Run NHST and determine the p value under the null hypothesis. Reject the null hypothesis if the p value is smaller than the level of statistical significance you decided. The null hypothesis is usually like “the difference of the means in the groups are equal to 0” or “the means of the two groups are the same”. And the alternative hypothesis is the counterpart of the null hypothesis. So, the procedure is fairly easy to follow, but there are several things you need to be careful about in NHST. Myths of NHST. There are several reasons why NHST recently gets criticisms from researchers in other fields.

The main criticism is that NHST is overrated. There are also some “myths” around NHST, and people often fail to understand what the results of NHST mean. First, I explain these myths, and the explain what we can do instead of / in addition to NHST, particularly effect size. The following explanations are largely based on the references I read. I didn't copy and paste, but I didn't change a lot either. I also picked up some of them which probably are closely related to HCI research.

I think they would help you understand the problems of NHST, but I encourage you to read the books and papers in the references section. This book explains the problems of NHST well and presents alternative statistical methods we can try. Beyond Significance Testing: Reforming Data Analysis Methods in Behavioral Research by Rex B. Refox xii full + keygen. (, the chapter 1 is available as ) There is a great paper talking about some dangerous aspects of NHST. This is another paper talking about some myths around NHST. Myth 1: Meaning of p value.

Let's say you have done some kinds of NHST, like t test or ANOVA. And the results show you the p value. But, what does that p value mean? You may think that p is the probability that the null hypothesis holds with your data. This sounds reasonable and you may think that is why you reject the null hypothesis.

The truth is that this is not correct. Don't get upset. Most of the people actually think it is correct. What the p value means is if we assume that the null hypothesis holds, we have a chance of p that the outcome can be as extreme as or even more extreme than we observed. Let's say your p value is 0.01. This means you have only 1% chance that the outcome of your experiment is like your results or shows a even clearer difference if the null hypothesis holds.

So, it really doesn't make sense to say that the null hypothesis is true. Then, let's reject the null hypothesis, and we say we have a difference. The point is that the p value does not directly mean how likely what the null hypothesis describes happens in your experiment. It tells us how unlikely your observations happen if you assume that the null hypothesis holds. So, how did we decided “how unlikely” is significant? This is the second myth of NHST. Myth 2: Threshold for p value.

You probably already know that if p. Another criticism that NHST has is that the test largely depends on the sample size. We can quickly test this in R. A = rnorm(10, 0, 2) b = rnorm(10, 1, 2) Here, I create 10 samples from two normal distributions: One with mean=0 and SD=2, and one with mean=1 and SD=2. Main dilli hoon tv serial episode 1.

If I do a t test, the results are: > t.test(a,b,var.equal=F) Welch Two Sample t-test data: a and b t = -0.8564, df = 17.908, p-value = 0.4031 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -2.855908 1.202235 sample estimates: mean of x mean of y -0.01590409 0.81093266 So, it is not significant. But what if I have 100 samples?

A = rnorm(100, 0, 2) b = rnorm(100, 1, 2) t.test(a,b,var.equal=F) Welch Two Sample t-test data: a and b t = -4.311, df = 197.118, p-value = 2.565e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -1.7016796 -0.6334713 sample estimates: mean of x mean of y -0.1399379 1.0276376 Now, p. Another common misunderstanding is that the p value indicates the magnitude of an effect.

For example, someone might say the effect with p = 0.001 has a stronger power than the effect with p = 0.01. This is not true. The p value has nothing with the magnitude of an effect. The p value is the conditional probability of the occurrence of the data you observed given the null hypothesis.