Statistical analysis is often used to examine survey results and experimental data.

A quantitative hypothesis contains a null proposition and an alternative that is proved or disproved through statistical analysis. The process speculates that an independent variable affects a dependent variable, and an experiment is conducted to see if there is a relationship between the two. This type of hypothesis is expressed in numerical terms and has specific rules and limits. The null hypothesis is rejected or accepted as a result of statistical data collected during a set of experiments.

One of the main differences between a qualitative hypothesis and a quantitative one is that it has very specific limits. An example of a null hypothesis might be “an additional five hours of study time per week leads to a higher grade point average in college students”. The alternative hypothesis would likely state “an additional five hours of study time per week does not increase college students’ grade point average.” To reject or accept the null hypothesis, experimental data would need to be recorded over a specified period of time.

Most studies that set out to test a quantitative hypothesis measure data based on statistical significance, which means that there is a low chance of error. In the case of proving or disproving the effect of study time on college student grade point averages, a control group would likely be tested. The behaviors and environments of these groups are generally controlled by the researchers. Data would also be obtained from a group of students whose behaviors and environments were not controlled.

Since a quantitative hypothesis and a research study depend on numerical data, the results of an experiment or research are translated into mathematical values. For example, many market research studies use scales that assign a numerical value to each response. A “agree” answer might match the number “4”, while a “disagree” reply might match the number “2”. When all survey feedback is recorded and analyzed, a percentage based on the total number of responses is assigned to each number.

Statistical analysis is often used to examine survey results and experimental data. The rejection or acceptance of the quantitative hypothesis depends on the numerical result of the analysis. For example, if the grade point average must be at least 3.5 to prove that the amount of study time has a direct effect, an average of 3.45 would result in the quantitative hypothesis being rejected.