Data Science Course > Hypothesis Testing

Statistical Hypothesis in Data Science

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A hypothesis is frequently referred to as a "informed guess" regarding a particular parameter or population. After it has been defined, data can be gathered to see if it provides sufficient evidence to support the hypothesis.

There are three types of statistical hypotheses:

  • Null Hypothesis - This is the default assumption that there is no relationship between two variables.
  • Alternative Hypothesis - This is when there is a relationship between two variables. The alternative hypothesis may be a statement about the strength or direction of the relationship between these variables.
  • Confidence Level - This represents how likely it would be for an outcome to occur if the null and alternative hypotheses were true.

Hypothesis testing is a statistical approach used by scientists and researchers to determine the validity of their claims concerning real-world/real-life events. In statistics and data science, hypothesis testing procedures are frequently used to determine if statements regarding the recurrence of occurrences are true.

There are many benefits of using a statistical hypothesis. One of the most important benefits is that it can be used to identify patterns in data and make predictions about what will happen in the future.

A statistical hypothesis test is a statistical inference procedure that is used to determine a probable conclusion from two competing hypotheses. A null hypothesis and an alternative hypothesis for the probability distribution of the data are proposed in a statistical hypothesis test.

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  2. Statistical Hypothesis in Data Science
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  3. Errors in Statistical Hypothesis in Data Science
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  4. Statistical Inference for Single Population in Data Science
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  5. Testing Example in Data Science
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  6. Statistical Inference with Two Population in Data Science
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