Statistical Hypothesis Tests
Statistical hypothesis tests are methods used in data science and artificial intelligence to determine whether a hypothesis about a population parameter is supported by the available evidence from a sample of data. The process involves formulating a null hypothesis, which is the assumption that there is no significant difference between the observed data and what would be expected under a given statistical model. An alternative hypothesis is also formulated, which is the hypothesis that there is a significant difference between the observed data and what would be expected under the null hypothesis. The statistical test is then performed to determine the probability of obtaining the observed data, or data more extreme, under the null hypothesis. If this probability is sufficiently small, the null hypothesis is rejected in favor of the alternative hypothesis. Statistical hypothesis tests are widely used in data science and artificial intelligence to make decisions based on data, such as determining whether a new machine learning algorithm is better than an existing one, or whether a new drug is more effective than an existing one.
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