A quantitative hypothesis involves a null and alternative proposition, tested through statistical analysis to determine if an independent variable affects a dependent variable. It has specific limitations and is expressed in numerical terms. Results are translated into mathematical values and analyzed through statistical analysis.
A quantitative hypothesis contains a null proposition and an alternative that is proved or disproved through statistical analysis. The process assumes 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 limitations. The null hypothesis is rejected or accepted as a result of statistical data collected during a series of experiments.
One of the main differences between a qualitative and a quantitative hypothesis is that it has very specific limitations. An example of a null hypothesis would be “five additional hours of study per week leads to a higher grade point average in college students.” The alternative hypothesis would probably state that “five additional hours of study per week does not increase the grade point average of college students.” To reject or accept the null hypothesis, the experimental data should be recorded over a certain period of time.
Most studies that aim 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 most likely be tested. The behaviors and environments of these groups are usually controlled by researchers. The data would also have been obtained from a group of students whose behaviors and environments were not monitored.
Because a quantitative hypothesis and research study are based on numerical data, the results of an experiment or investigation are translated into mathematical values. For example, much market research uses scales that assign a numerical value to each response. An “agree” answer can correspond to the number “4”, while a “disagree” answer can correspond to the number “2”. When all survey feedback is logged and analyzed, each issue is assigned a percentage based on the total amount of responses.
Statistical analysis is often used to examine the results of surveys 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 show that the amount of study time has a direct effect, a grade point average of 3.45 would result in a rejection of the quantity hypothesis.
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