Section 6.1 Hypothesis Testing
When evaluating social phenomena, especially regarding crime, people often make judgments. However, such judgments are not always correct. Researchers conducting empirical studies make tentative statements about phenomena and make decisions about the validity of those statements. These tentative statements are called hypotheses, and if one denies a hypothesis that happens to be true, they commit an error in judgment.
When making decisions based on sample statistics about attributes of a population, i.e., unknown facts, one must judge whether something is like that or not. However, such judgments are not always accurate and can sometimes be wrong. In other words, researchers may make errors in judgment.
Specifically, hypothesis testing entails comparing empirically observed sample findings with the theoretically expected outcomes if the null hypothesis were true. The null hypothesis represents the hypothesis that a researcher aims to reject, thereby supporting its alternative. This hypothesis often posits that two or more variables are not related. To compare the null and alternative hypotheses, the researcher calculates the probability of the observed outcome occurring solely due to chance or random error (Vogt & Johnson, 2011).
Subsection 6.1.1 NHST Steps
This hypothesis testing is also known as null hypothesis significance testing, or NHST. In the context of NHST, it is recommended to follow these five steps:
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Formulate the null and alternative hypotheses.
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Calculate the test statistic.
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Determine the probability (p-value) of obtaining a test statistic at least as extreme as the observed value, assuming no relationship exists.
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If the p-value is very small, typically less than 5%, reject the null hypothesis.
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If the p-value is not small, typically 5% or greater, retain the null hypothesis.
