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21 Cards in this Set
- Front
- Back
- 3rd side (hint)
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What are Hypotheses? |
Hypotheses are propositions to underline an analysis. |
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4 Steps of Hypothesis Testing |
1. State the Hypothesis 2. Form an analysis plan (design the statistical test) 3. Analyze the Sample formuData (Calculate the test Statistic) 4. Interpret the Results (Reject or fail to Reject the null hypothesis) |
State, Form, Analyze, Interpret |
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What are the Components of Stating the Hypothesis. |
Null Hypothesis and Alternative Hypothesis. Both are mutually exclusive and therefore, rejecting the null hypothesis is interpreted as supporting the alternative hypothesis. |
Mutually Exclusive |
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What is Null Hypothesis |
The statement that the analyst attempts to reject |
Reject |
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What is Alternative Hypothesis |
The opposite claim to Null hypothesis and represents the behavior that exists if the null hypothesis is false. |
Opposite and behavior that exist. |
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What is Test Statistics |
A test statistic is a standardized value that is calculated from sample data during a hypothesis test. It determine whether or not to reject the null hypothesis. It compares your data with what is expected under the null hypothesis. |
Large Values = Evidence against Null Hypothesis. |
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What are Significance Level |
Significance Level is the probability of rejecting the null hypothesis in a statistical test when it is true. They are often set at 1%, 5% and 10% |
Probability of rejecting the null hypothesis |
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What are Confidence Interval |
It is a range of values so defined that there is a specified probability that the value of a parameter lies within it.With a confidence interval, we report a range of numbers, inwhich we hope the true parameter will lie. |
Confidence Level = 100% - Significance Level. |
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Test Statistic formula |
Test Statistic is calculated from the data and is compared against the predetermined critical value. |
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What is the Standard Error of Statistic |
It is defined as the standard deviation of the sample statistic and is a measure of the precision of the statistic. |
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What does Test Statistic do? |
It quantifies how far the estimated value is from the hypothesized value, in standard deviation units. |
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What does p-Value imply? |
It implies that assuming the null hypothesis is true, there is a "x%" chance of finding a value as extreme as the one derived from the sample. |
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Decision rule to reject Null Hypothesis |
Reject Null Hypothesis if ; Test Statistic > Critical Value or P-value < significance level Test Stat and P-Value has inverse relationship. |
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4 Common problems using Inferential Statistics |
1. Strength of Relationships 2. Economic Significance 3. Distribution Assumption 4. Level of Confidence |
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Problem of Strength of Relationship |
p-values are often mistaken to indicate stronger relationship. p-value is not a measure of strength but that of existence of relationship. |
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Problem of Economic Significance |
Sometimes variable that establish no statistical significance could have substantial economic significance. Test stat exceed critical value because the standard error is small not because the estimate is large. |
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problem of distribution assumption |
p-value is calculated assuming the data follow a particular kind of distribution (normal). It wont be meaningful if the data violates the distribution. |
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problem of level of confidence |
confidence level does not equal the probability that a relationship exist. |
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Type I Error |
A Type I error occurs when rejecting a true null hypothesis. Probability of Type I error is denoted by "α - Alpha". α is the significance level. "1-α" is the confidence level. |
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Type II Error |
Type II error occurs when failing to reject an untrue null hypothesis. Probability of Type I error is denoted by "β - Beta". "1- β" is the statistical power of the test. |
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Type I and Type II error in Hypothesis testing |
1. Reject the Null when Null is true = Type I error. 2. Fail to reject the null when null is false = Type 2 error. 3. Reject the Cull when null is false and Fail to reject the null when null is true = "correct decision" |
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