Basic statistics for data analyst8/8/2023 ![]() Null hypothesis: the average weight of European students (sample) is equal to the average weight of European adults (population).Now we can form our null and alternative hypothesis: Hence, for the sake of demonstration, we’re going to assume that the population standard deviation is equal to sample standard deviation. However, we still don’t know its standard deviation. stated that the average weight of European adults is 70.8 kg. How do we know the population mean and standard deviation?Ī research conducted by Walpole et al. This is the use case that can be answered with the Z-test because we fulfill the following conditions:īut there is a catch here. So, let’s take a look at the dataset first. ![]() To answer this question, we’re going to show you different types of statistical tests available out there and when you’re going to need each of them with one example dataset as our use case. So the natural question that comes next is, which type of statistical test should we choose considering the problem and data that we have? Which type of statistical test we should apply is totally dependent on our use case and data. The problem is, there are various test statistics out there. If the p-Value is larger than our significance level, we go with our null hypothesis. The general idea is that if the resulting p-Value is less than our significance level, we reject the null hypothesis. Meanwhile, we need to conduct test statistics in order to find the p-Value. We can set the value of significance level in advance, for example 0.05. Now, to know whether or not we should reject the null hypothesis, it depends on two factors: We can either reject the null hypothesis in favor of the alternative hypothesis, or go with our null hypothesis. Since the null hypothesis is always going to be our default value, we cannot ‘accept’ the null hypothesis. Null Hypothesis vs Alternative Hypothesis: Which Statistical Test to Choose? In the following section, we’re going to discuss how we can formulate our null hypothesis and alternative hypothesis. Given our data, how can we properly form a null hypothesis and an alternative hypothesis? Which type of statistical test should we perform? However, everything up to now might seem a little abstract for you. So far you’ve seen the general approach on how to conduct hypothesis testing. If the p-Value is higher than our significance level, then we go with our null hypothesis. If the p-Value is smaller than our significance level, then we reject the null hypothesis in favor of our alternative hypothesis.
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