Topic 8.4 Statistical Inference, Statistical Significance and Hypothesis Testing

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HBS108 (Topic 8.4 Statistical inference and hypothesis testing: Conf) Mind Map on Topic 8.4 Statistical Inference, Statistical Significance and Hypothesis Testing, created by shirley.ha on 09/15/2013.

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Created by shirley.ha about 6 years ago
Casuality aka causation & association
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Null hypoth, chi-squares, p-values, CI
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P-values, Generalisability, Study Limits
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P-values
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Topic 8.4 Statistical Inference, Statistical Significance and Hypothesis Testing
1 LO
1.1 The basic concepts of statistical inference and hypothesis testing, where we analyze the sample data to examine a relationship between variables and make inferences about the population.
1.1.1 Chi-Square (X2), confidence intervals (CI) and p-values
1.2 The issue of causation that assists us to interpret the results from a research studies.
2 Stat Inference
2.1 Makes use of information from a sample to draw conclusions (inferences) about the population from which the sample was taken.
2.2 Applies this process to datasets with calculated degrees of uncertainty
2.2.1 established that there is H1 or is not H0 association between the variables
2.2.1.1 interested in (independent and dependent), we have to test this association to find out if this is a statistically significant association.
2.3 Whole purpose of research
2.3.1 use data from a sample to make inferences about the whole population from which the sample was drawn because we are usually unable to conduct our research on the whole population.
2.3.1.1 the results from our analysis of this sample data, true (or statistically significant) for the whole population?”
3 Stat sign
3.1 specific meaning in research statistics and it come in two varieties:
3.2 1. Statistical significance:
3.2.1 when the p-value is small enough to reject the null hypothesis of no effect
3.3 2. Clinical importance:
3.3.1 when the effect size is large enough to be potentially considered worthwhile by patients.
4 Alternative Hypothesis (H1)
4.1 tentative theory, supposition (also known as the “hypothesis” quantitative researchers state at the beginning of the research process)
4.2 provisionally adopted to guide the practical steps we will use to conduct the research study.
4.3 hypothesis is statistically tested and potentially refuted in the analysis/interpretation step
4.4 we never state the hypothesis as an affirmative statement
4.4.1 always stated in the negative and is called the Null Hypothesis
4.5 If statistically proven, H1 states there IS a relationship between the IV and the DV
5 Null Hypothesis
5.1 typically proposes a general or default position, such as that “there is no relationship between two variables”
5.2 1. typically proposes a general or default position, such as that “there is no relationship between two variables”
5.3 2. That “there is no difference between the exposed and unexposed group” (i.e. in a cohort study)
5.4 3. That “there is no difference between the cases and control groups” (i.e. in a case control study).
6 H0 states there is NO relationship between the IV and the DV

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