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  1. A hypothesis test evaluates two mutually exclusive statements about a population to determine which statement is best supported by the sample data.[1]
  2. When we say that a finding is statistically significant, it’s thanks to a hypothesis test.[1]
  3. Usually, the probability value is set at 0.05: the null hypothesis will be rejected if the probability value of the statistical test is less than 0.05.[2]
  4. The alpha is set by the researcher, usually at .05, and is the chance the researcher is willing to take and still claim the significance of the statistical test.).[2]
  5. In this blog post, I explain why you need to use statistical hypothesis testing and help you navigate the essential terminology.[3]
  6. Statistical power is the probability that a hypothesis test correctly infers that a sample effect exists in the population.[3]
  7. The alternative hypothesis, H a , is a statement of what a statistical hypothesis test is set up to establish.[4]
  8. Note that by default the test procedure provides descriptive statistics of the variable and the hypothesis test results.[5]
  9. For a statistical test to be valid, it is important to perform sampling and collect data in a way that is designed to test your hypothesis.[6]
  10. If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p-value.[6]
  11. Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p-value.[6]
  12. In most cases you will use the p-value generated by your statistical test to guide your decision.[6]
  13. This article gives an overview of basic steps in the general procedure for statistical hypothesis testing and points out some common pitfalls and misconceptions.[7]
  14. Statistical hypothesis testing is a widely used method of statistical inference.[7]
  15. A statistical hypothesis testing is a procedure that involves formulating a statistical hypothesis and using a sample data to decide on the validity of the formulated statistical hypothesis.[7]
  16. The probability of making a Type I error is usually called alpha (α), and that value is determined in advance for any hypothesis test.[7]
  17. In this situation, we specify a 2-sided statistical test, in which we accept a combined rate of false-positives (for both the higher and lower level of the outcome) of only 5%.[8]
  18. When presenting the results of a hypothesis test, include the descriptive statistics in your conclusions as well.[9]
  19. In the first step of the hypothesis test, we select a level of significance, α, and α= P(Type I error).[10]
  20. We then determine the appropriate test statistic (Step 2) for the hypothesis test.[10]
  21. A hypothesis test can be performed on parameters of one or more populations as well as in a variety of other situations.[11]
  22. A goodness-of-fit test refers to a hypothesis test in which the null hypothesis is that the population has a specific probability distribution, such as a normal probability distribution.[11]
  23. Some researchers say that a hypothesis test can have one of two outcomes: you accept the null hypothesis or you reject the null hypothesis.[12]
  24. Two types of errors can result from a hypothesis test.[12]
  25. What is meant by a statistical test?[13]
  26. A statistical test provides a mechanism for making quantitative decisions about a process or processes.[13]
  27. A classic use of a statistical test occurs in process control studies.[13]
  28. A p value is a number that you get by running a hypothesis test on your data.[14]
  29. In statistics, a hypothesis test calculates some quantity under a given assumption.[15]
  30. The assumption of a statistical test is called the null hypothesis, or hypothesis 0 (H0 for short).[15]
  31. There are two common forms that a result from a statistical hypothesis test may take, and they must be interpreted in different ways.[15]
  32. A statistical hypothesis test may return a value called p or the p-value.[15]
  33. Statistical hypothesis testing plays an important role in the whole of statistics and in statistical inference.[16]
  34. When theory is only capable of predicting the sign of a relationship, a directional (one-sided) hypothesis test can be configured so that only a statistically significant result supports theory.[16]
  35. The successful hypothesis test is associated with a probability and a type-I error rate.[16]
  36. Those making critical decisions based on the results of a hypothesis test are prudent to look at the details rather than the conclusion alone.[16]

소스

  1. ↑ 1.0 1.1 Understanding Hypothesis Tests: Why We Need to Use Hypothesis Tests in Statistics
  2. ↑ 2.0 2.1 LibGuides at La Trobe University
  3. ↑ 3.0 3.1 Statistical Hypothesis Testing Overview
  4. ↑ Tests of Significance
  5. ↑ Hypothesis Tests
  6. ↑ 6.0 6.1 6.2 6.3 A Step-by-Step Guide with Easy Examples
  7. ↑ 7.0 7.1 7.2 7.3 Statistical hypothesis testing and some pitfalls
  8. ↑ Hypothesis Testing
  9. ↑ Hypothesis Testing
  10. ↑ 10.0 10.1 Hypothesis Testing for Means & Proportions
  11. ↑ 11.0 11.1 Statistics - Hypothesis testing
  12. ↑ 12.0 12.1 Hypothesis Tests
  13. ↑ 13.0 13.1 13.2 7.1.3. What are statistical tests?
  14. ↑ Hypothesis Testing
  15. ↑ 15.0 15.1 15.2 15.3 A Gentle Introduction to Statistical Hypothesis Testing
  16. ↑ 16.0 16.1 16.2 16.3 Statistical hypothesis testing

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