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Chapter 2 Statistical Inference, Exploratory Data Analysis, and the Data Science Process
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INFERENTIAL STATISTICS AND HYPOTHESIS TESTING We use inferential statistics because it allows us to measure behaviour in samples to learn more about the behavior in populations that are often too large or inaccessible We use samples because we know how they are related to populations. For example, suppose the average score on a standardized exam in a given population is 1,000
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Example of standardized exam scores The method in which we select samples to learn more about characteristics in a given population is called hypothesis testing. Hypothesis testing is really a systematic way to test claims or ideas about a group or population.
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Hypothesis Testing Hypothesis testing is the method of testing whether claims or hypotheses regarding a population are likely to be true.
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FOUR STEPS TO HYPOTHESIS TESTING Step 1:State the hypotheses. Step 2: Set the criteria for a decision. Step 3:Compute the test statistic. Step 4: Make a decision
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FOUR STEPS TO HYPOTHESIS TESTING Step 1:State the hypotheses. We begin by stating the value of a population mean in a null hypothesis, which we presume is true. For the children watching TV example, we state the null hypothesis that children in the United States watch an average of 3 hours of TV per week. This is a starting point so that we can decide whether this is likely to be true, similar to the presumption of innocence in a courtroom. When a defendant is on trial, the jury starts by assuming that the defendant is innocent. The basis of the decision is to determine whether this assumption is true. Likewise, in hypothesis testing, we start by assuming that the hypothesis or claim we are testing is true. This is stated in the null hypothesis. The basis of the decision is to determine whether this assumption is likely to be true.
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FOUR STEPS TO HYPOTHESIS TESTING Step 1:State the hypotheses. An alternative hypothesis (H1) is a statement that directly contradicts a null hypothesis by stating that that the actual value of a population parameter is less than, greater than, or not equal to the value stated in the null hypothesis.
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  • Spring '17
  • azmat fatma
  • Data Mining, Null hypothesis, Statistical hypothesis testing, researcher

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