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Theresearch question: Is learning of 6th graders on a math lessonaffected by background noise level? Many notions have been discussedwith regards to the effect of noise on learning of the students. Forexample, children who had higher noise levels tend to performsignificantly worse (Pujol et l., 2014).

TheNull Hypothesis (H0): The performance of 6th graders in mathematicsis not different between students in high and low background noiselevel. The Alternative Hypothesis (H1): The performance of sixthgraders in mathematics is different between students in high and lowbackground noise level.

Thedependent variable in this study will be mathematics performance,which will be continuous in nature and measured as the score that thestudents will get in the math exam. This is important because for anindependent sample t-test to be carried out the dependent variablehas to be continuous (Angrist, 2012). The independent variable willbe noise level that will be on two levels that are the high noiselevel and the low noise level. The 6th-grade students can be dividedinto two equal groups where one group will learn in low backgroundnoise levels and the other group in high background noise level. Thesame math exam will be administered at the end and the scoresrecorded for both groups.

Asthe samples size increases, the confidence in the estimates increasesgiven that uncertainty decreases hence resulting in more precision(Marshall et al., 2013). For example, if the study was to know howmany people smoke in an area, a large sample size will give resultinto more accurate values than a small sample size. Also increasingthe sample size gives the power to detect differences easily. Forinstance, a large sample size is used when a study aims atinvestigating if there is a difference in the number of men and womenwho smoke in the area. A large sample size will more likely give aclear difference. Therefore, there is need to have proper plan forthe sample size before embarking on data collection because it helpsavoid spending resources on collecting more data than is needed. Italso helps in getting the correct sample size that will give a lot ofprecision (Marshall et al., 2013).

References

Angrist,J. D. (2012). Estimation of limited dependent variable models withdummy endogenous regressors.&nbspJournalof business &amp economic statistics.

Marshall,B., Cardon, P., Poddar, A., &amp Fontenot, R. (2013). Does samplesize matter in qualitative research?: A review of qualitativeinterviews in IS research.&nbspJournalof Computer Information Systems,&nbsp54(1),11-22.

Pujol,S., Levain, J. P., Houot, H., Petit, R., Berthillier, M., Defrance,J., … &amp Mauny, F. (2014). Association between ambient noiseexposure and school performance of children living in an urban area:a cross-sectional population-based study.&nbspJournalof Urban Health,&nbsp91(2),256-271.