Consider the research question below

Which are likely to be answered using "quantitative" research studies, and which are likely to be answered using "qualitative" research studies?

1. What percentage of the population experience minor side-effects from this medication?

2. Why do people opt to receive holistic medicine?

Why do people opt to receive holistic medicine?

A. Not reliable, B. Not valid: A lack of reliability would mean inconsistent measurements across repeated trials, which is not demonstrated when the same value is obtained three times. Similarly, if the measured value matches the standard, it cannot be described as invalid.

B. A. Reliable, B. Not valid: Although repeated identical measurements indicate reliability, stating that the result is not valid would imply inaccuracy compared to the standard. When the measured value equals the accepted standard weight, the measurement meets criteria for validity, so this pairing does not correctly describe the scenario.

C. A. Reliable, B. Valid: Reliability refers to consistency or repeatability of a measurement. If measuring a cup of rice three times yields the same result each time, the measurement demonstrates consistency, which defines reliability. Validity refers to accuracy—whether the measurement reflects the true or accepted standard value. If the measured weight matches the known standard of 5 grams, the measurement is accurate and therefore valid.

D. A. Not reliable, B. Valid: If measurements are identical across repeated trials, the process demonstrates reliability rather than unreliability. While matching the standard does indicate validity, describing the repeated consistent results as not reliable conflicts with the definition of reliability as consistency in measurement.

The alternative hypothesis (H₁) is a statement that proposes a relationship or effect that the researcher expects to find in the study. It is formulated based on theory, prior evidence, or a research question and serves as the basis for statistical testing. While it is assumed to be plausible, it has not yet been proven; the purpose of hypothesis testing is to evaluate whether the data provide sufficient evidence to support or reject it.