## 1.5 Two approaches to RQs

RQs can be approached in one of two ways:

- For
**estimation**(*confidence intervals*): These RQs are concerned with, for example, estimating a value in a population. This value may be the size of a difference (probably a RQ with a Comparison), or strength of a relationship (probably a RQ with a Connection). - For
**making decisions**(*hypothesis testing*): These RQs are concerned with making a decision about an unknown population value: for example, is the percentage the same in two different groups of the population?

**Lemma 1.7 (RQ type) **What approach do these RQs take:
Decision-making, or Estimation?

- Among Australian upper-limb amputees, is the percentage wearing prosthesis 'all the time' the same for transradial and transhumeral amputations? (Davidson 2002)
- In New York, what is the difference between the average height of oaks trees (ten weeks after planting) comparing trees planted in a concrete sidewalk and a grassed sidewalk? (Grabosky and Bassuk 2016)
- What is the average response time of paramedics to emergency calls? (Pons et al. 2005)
- Is there a relationship between the average weekly hours of physical activity in children and the weekly maximum temperature? (Edwards et al. 2015)

### 1.5.1 Estimation: Confidence intervals questions

Sometimes,
the RQ concerns how precisely a *value* in the population
is estimated by the sample.
This value may measure a *difference*,
or the *strength* of a relationship.

These types of RQs are studied in more depth later on in this subject.

**Example 1.19 (Estimation RQs) **Consider this RQ
(based on Barrett et al. (2010)):

Among Australian teens with a common cold,

how much shorterare cold symptoms, onaverage, for teens taking a daily dose of echinacea compared to teens taking no medication?

This RQ
asks about size of the *difference* (in the *population*)
between the average duration of cold symptoms.

### 1.5.2 Making decisions: Hypothesis testing questions

Sometimes,
RQs are not about the precision with which a population value
is estimated by the sample,
but instead about deciding if a difference or a relationship exists
in the *population*.

These RQs often are associated
with *hypotheses*:
*statements* that suggest possible answers to the RQ.
Based on the sample,
the hypothesis best supported by the data is to be chosen.

These types of RQs are studied in more depth later on in this subject.

**Example 1.20 (Making decisions with samples) **Consider this RQ
(based on Barrett et al. (2010)):

Among Australian teens with a common cold, is the average duration of cold symptoms shorter for teens taking a daily dose of echinacea compared to teens taking no medication?

This is a decision-type RQ,
with two possible answers
(Fig. 1.3):
Either echinacea *does* result in shorter average cold durations,
or it *doesn't*.
In practice the answer is rarely clear cut,
and instead *how much* evidence
there is in the sample to support a particular hypothesis
about the *population* is reported.

*support*or

*contradict*a hypothesis; evidence rarely

*proves*a hypothesis (at least, without any other support, such as theortical support). Ultimately, after collecting data from a

*sample*, a decision must be made about which explanation about the

*population*is more consistent with the data collected.

### References

*Annals of Internal Medicine*153 (12): 769–77.

*Journal of Hand Therapy*15 (1): 62–70.

*Journal of Physical Activity and Health*12 (8): 1074–81.

*Urban Forestry & Urban Greening*16: 103–9.

*Academic Emergency Medicine*12 (7): 594–600.