It's below a threshold, maybe this threshold is 5%, if the probability of that And the reason why this makes sense is if the probability of getting the statistic from a sample of a certain size, if we assume that the null hypothesis is true is reasonably low if And when we reject our null hypothesis, some people will say that might suggest the alternative hypothesis. Our significance level, alpha, then we fail to reject, fail to reject our null hypothesis. Videos if our p-value is less than our significance level, then we reject our null hypothesis, and if our p-value is greater than or equal to This should all be review, we introduced it in other videos. That we set ahead of time which is known as the significance level, then we reject the null hypothesis. Is often known as a p-value, is below some threshold If we were to assume that our null hypothesis, if our null hypothesis is true. And then using that statistic, we try to come up with the probability of getting that statistic, the probability of getting that statistic that we just calculated from that sample of a certain size, given

That's trying to estimate the parameter in question. Using that sample, weĬalculate a statistic, we calculate a statistic, Whether we want to reject or fail to reject the null hypothesis, we take a sample. And to test it, and we're really testing the null hypothesis. Tends to be kind of what was always assumed or the status quo while the alternative hypothesis, hey, there's news here, there's something alternative here.

This will say some hypothesesĪbout a true parameter for this population. So just as a little bit of review, in order to do a significance test, we first come up with a null and an alternative hypothesis. Gonna do in this video is talk about Type I errors and Type II errors and this is in the context

Type ii error