We have studied forming confidence intervals, which answer estimation-type RQs, and indicate the precision with which a statistic estimates a parameter.
The word hypothesis means 'a possible explanation'.
Scientific hypotheses usually refer to the explanations that scientists or engineers are wishing to demonstrate are true; for example, an engineer may expect that replacing sand with glass in the manufacture of concrete produces desirable characteristics.436
Statistical hypotheses refer to the null hypothesis and the alternative hypothesis that are necessary for formal statistical hypothesis testing. These refer to the two possible statistical explanations for the difference between the proposed population parameter and the observed sample statistic.
We are discussing statistical hypotheses.
Assumption: Make an assumption about the population.
Expectation: Based on this assumption, the distribution of the possible values of the sample statistic can be described.
Observation: If sample information is observed that is:
- unlikely to happen by chance, it is contrary to that assumption about the population parameter, and the assumption is probably wrong. There is evidence to suggest that the assumption is wrong (but it is not certainly wrong).
- likely to happen by chance, it is consistent with that assumption about the population parameter, and the assumption may be correct. There is no evidence to suggest the assumption is wrong (though it may be wrong).
|Estimation type (CI)||Decision type (Tests)|
|Proportions for one sample||Chap. 20|
|Means for one sample||Chap. 22||Chap. 28|
|Mean differences (for paired data; within-individual comparisons)||Chap. 23||Chap. 30|
|Relational/Interventional RQs (Comparison)|
|Means for two samples (between-individuals comparisons)||Chap. 24||Chap. 31|
|Odds for two samples, comparing ORs (between-individuals comparisons)||Chap. 25||Chap. 32|
|Relational/Interventional RQs (Connection)|
|Regression||Sect. 36.7||Sect. 36.7|