Uso interativo de planilha eletrônica para o ensino de Estatística: O caso do valor de p.
Interactive use of spreadsheets to teach statistics: The case for the p value

Fernando Frei

Resumo


O objetivo deste artigo foi avaliar os efeitos do uso de planilha eletrônica interativa para simular eventos na aprendizagem do conceito estatístico inferencial do valor de p. O presente estudo possui uma abordagem metodológica quantitativa e qualitativa, baseada na descoberta guiada, em que os alunos recebem uma série de atividades que os levam a um objetivo predeterminado. Os resultados obtidos neste estudo demonstram que o procedimento adotado pode ser um aliado no ensino da inferência, e que a simulação torna as atividades mais ativas permitindo que os alunos descubram os próprios princípios, tornando o aprendizado mais efetivo.


Palavras-chave


Valor de p, Planilha Eletrônica, Inferência, Simulação

Texto completo:

PDF

Referências


ANDERSON-COOK, C. M.; DORAI-RAJ, S. Making the Concepts of Power and Sample Size Relevant and Accessible to Students in Introductory Statistics Courses using Applets. Journal of Statistics Education v. 11, n. 3, p. 1-12, 2003.

AQUILONIUS, B. C., BRENNER, M. E. Students’ reasoning about p-values. Statistics Education Research Journal, v. 14, n. 2, p. 7-27, 2015.

BADENES-RIBERA, L.; FRIAS-NAVARRO, D.; IOTTI, B.; BONILLA-CAMPOS, A.; LONGOBARDI, C. Misconceptions of the p-value among Chilean and Italian Academic. Psychologists, v. 7, p. 1-7, 2016.

BATANERO, C.; DIAZ, C. Methodological and Didactical Controversies around Statistical Inference (2006). Proceedings of 38th Conference of the French Statistical Conference. Actes du 36iémes Journées de la Societé Française de Statistique. Paris: Societé Française de Statistique. Disponível em . Acesso em: 10 fevereiro de 2018.

BIAU, D. J.; JOLLES, B. M. PORCHER, R. P Value and the Theory of Hypothesis Testing: An Explanation for New Researchers, Clin Orthop Relat Res, v. 468, p. 885-892, 2010.

CAÑADAS, G. R.; BATANERO, C.; DIAZ, C.; ROA, R. Psychology students’ understanding of the chi-squared test. Statistique et Enseignement, v. 3, n. 1, p. 3-8, 2012.

CHANDRAKANTHA, L. Simulation using excel data tables in teaching introductory statistics. Journal of Computing Sciences in Colleges, v. 29, p. 29-34, 2014.

COHEN, J. The earth is round (p < 0.05). American Psychologist, v. 49, n. 12, p. 997-1003, 1994.

CUMMING, G. (2010). Understanding, teaching and using p values. In A. Rossman & B. Chance (Eds.), ICOTS-10 Proceedings. Disponível em https://iase-web.org/documents/papers/icots8/ICOTS8_8J4_CUMMING.pdf. Acesso em: 10 fevereiro de 2018.

CURRAN-EVERETT, D. Explorations in statistics: hypothesis tests and P values. Adv Physiol Educ, v. 33, p. 81-86, 2009.

DE JONG, T.; VAN JOOLIGEN, W. Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, v. 68, n. 2, p. 179-201, 1998.

DELMAS, R.; GARFIELD, J.; CHANCE, B. A model of classroom research in action: Developing simulation activities to improve students’ statistical reasoning. Journal of Statistics Education, v. 7, n. 3, 1999. Disponível em: < https://ww2.amstat.org/publications/jse/secure/v7n3/delmas.cfm>. Acesso em: 10 de janeiro de 2018.

DUFFY, S. Random Numbers Demonstrate the Frequency of Type I Errors: Three Spreadsheets for Class Instruction. Journal of Statistics Education, v. 18, n. 2, p. 1-16, 2010.

ERICKSON, T. Using simulation to learn about inference. Proceedings of the Seventh International Conference on Teaching Statistics, ICOTS-7, 2006. Disponível em: https://www.researchgate.net/profile/Tim_Erickson2/publication/248282382_USING_SIMULATION_TO_LEARN_ABOUT_INFERENCE/links/5694828c08ae425c6896488d/USING-SIMULATION-TO-LEARN-ABOUT-INFERENCE.pdf. Acesso em: 18 de janeiro de 2018.

FALK, R. Misconceptions of statistical significance. Journal of Structural Learning, v. 9, p. 83-96, 1986.

GOODMAN S. A dirty dozen: twelve p-value misconceptions. Semin Hematol, v. 45, n. 3, p. 135-40, 2008.

GOODMAN, S. Commentary: The P-value, devalued. International Journal of Epidemiology, v. 32, p. 699-702, 2003.

GREENLAND, S.; SENN, S. J.; ROTHMAN, K. J.; CARLIN, J. B.; POOLE, C.; GOODMAN, S. N.; ALTMAN; D. G. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations, Eur J Epidemiol, v. 31, p. 337-350, 2016.

HALLER, H.; KRAUSS, S. Misinterpretations of significance: A problem students share with their teachers? Methods of Psychological Research, v. 7, n. 1, p. 1-20, 2002.

HATTIE, J.; TIMPERLAY, H. The power of feedback. Review of Educational Research, v. 77, n. 1, p. 81-112, 2007.

HUBBARD, R.; LINDSAY, R. M. Why P values are not a useful measure of evidence in statistical significance testing. Theory Psychol, v. 18, p. 69-88, 2008.

HUNT, N.; MASHHOUDY, H. Charts in Excel – A Series Matter. Teaching Statistics, v. 26, n. 2, p. 49-53, 2004.

JAMIE. D. M. Using Computer Simulation Methods to Teach Statistics: A Review of the Literature, Journal of Statistics Education, v. 10, n. 1, p. 1-20, 2002.

KLAHR, D.; NIGAM, M. The equivalence of learning paths in early science instruction. Psychological Science, v. 15, n. 10, p. 661-667, 2004.

LANE, D. M.; PERES, S. C. Interactive simulations in the teaching of statistics: Promise and Pitfalls. Proceedings of the Seventh Annual Meeting of the International Conference on the Teaching of Statistics, Salvador, Brazil, 2006. Disponível em: . Acesso em: 10 de fevereiro de 2018.

LANG, J. M.; ROTHMAN, K.; CANN, C. I. That confounded P-value. Epidemiology, v. 9, p. 7-8, 1998.

LE D. Bringing Data to Life into an Introductory Statistics Course with GAPMINDER. Teaching Statistics, v. 35, n. 3, p. 114-122, 2013.

MARTIN, D.; COLLEGE, D. A. Spreadsheet Tool for Learning the Multiple Regression F-test, t-tests, and Multicollinearity. Journal of Statistics Education, v. 16, n. 3, 2008. Disponível em: . Acesso em: 15 de fevereiro de 2018.

MILLS, J. Using computer simulation methods to teach statistics: A review of the literature. Journal of Statistics Education, v. 10, n. 1, p. 1-10, 2002.

MURPHY, S. J. (2009). The power of visual learning in secondary mathematics education. Pearson Education Inc. Disponível em . Acesso em: 09 de janeiro de 2018.

NUZZO, R. Scientific method: Statistical errors. Nature, v. 506, p. 150 –152, 2004.

QUINTELA-DEL-RÍO, A.; FRANCISCO-FERNÁNDEZ, M. Excel Templates: A Helpful Tool for Teaching Statistics. The American Statistician, V. 71, p. 317- 325, 2014.

SALEHI, M. Using MS Excel in Teaching Design of Experiment. International Journal of Education and Learning Systems, v. 1, p. 93 – 98, 2016.

SCHMIDT, F. L.; HUNTER, J. E. What If There Were No Significance Tests? Eight common but false objections to the discontinuation of significance testing in analysis of research data. HARLOW L., MULAIK S. e STEIGER J. (Eds.) New York. Routledge, p. 37-64, 1997.

SEBASTIANI, R. G.; VIALI, L. Teste de Hipóteses: uma análise dos erros cometidos por alunos de engenharia. Bolema, v. 24, n. 40, p. 835-854, 2011.

SOTOS, A. E. C.; VANHOOF, S.; NOORTGATE, W. V.; ONGHENA, P. Students’ misconceptions of statistical inference: A review of the empirical evidence from research on statistics education. Educational Research Review, v. 2, p. 98-113, 2007.

STERNE, J. A. C. Teaching hypothesis tests – time for significant change? Statistics in Medicine, v. 21, p. 985-994, 2002.

VALLECILLOS, A. Empirical evidence about understanding of the level of significance concept in hypotheses testing by university students. Themes in Education, v. 3, n. 2, p. 183-198, 2002.

ZACHAROPOULOU, H. Two Learning Activities for a Large Introductory Statistics Class. Journal of Statistics Education, v. 14, n. 1, p. 1-10, 2006.

ZIEFFLER, A.; GARFIELD, J.; DELMAS, R.; READING, C. A framework to support research on informal inferential reasoning. Statistics Education Research Journal, v. 7, n. 2, p. 40-58, 2008.




DOI: https://doi.org/10.23925/1983-3156.2018v21i2p187-201

Métricas do artigo

Carregando Métricas ...

Metrics powered by PLOS ALM


INDEXADORES DA REVISTA