This also affects what type of graphs and tables you can use and influences your choices of statistical tests. Tables and graphs may be drawn using the computer. No matter what graph you choose, it must accurately reflect the data in relation to the prediction of the research hypothesis. One graph is sufficient. Do not produce irrelevant graphs (that have nothing to do with your research). The report should not include graphs showing each individual participant’s score. Label each part of the graph. Vertical (y-axis, DV) Horizontal (x-axis, IV) Bar-graphs are good to compare categories. Histograms are good for a continuous IV. F. Results: Inferential Statistics Even if you suffer from calculatophobia (fear of statistics), you must include inferential statistical analysis of your results in this section. You probably have some idea of the results that you wanted from your experiment. Do not try to be a cheerleader rooting for your specific results. In research, you want to be a non-partisan (not taking sides) bystander. Don’t be so in love with your outcome that you lose your professional objectivity and become biased in your evaluation of the data. You must interpret the data regardless of the outcome—even if you failed to show a significant change between rock music and classical music (for example). Inferential statistics ask if your random sample really represents the whole population. Can we infer from the small group how the large group would behave? Example: Could I ask 10% of the class to place a vote on something, and then apply that vote to the entire class? Could 1 handful of beans tell me the health and taste of the entire bin of beans? If 10 states voted for one presidential candidate, could we infer that the rest of the country will likely vote the same way? Is it likely that the math anxiety experienced by economics majors could be applied to the entire population of people majoring in economics that could have been sampled? The Null Hypothesis (N0) The Null Hypothesis (N0) assumes that there is no significant difference in your data. In statistics, The Null is assumed to be true…your experiment is either proving the Null to be right or wrong. Think of this like a court of law where you are assumed innocent until proven guilty.
This also affects what type of graphs and tables you can use and influences your choices of statistical tests. Tables and graphs may be drawn using the computer. No matter what graph you choose, it must accurately reflect the data in relation to the prediction of the research hypothesis. One graph is sufficient. Do not produce irrelevant graphs (that have nothing to do with your research). The report should not include graphs showing each individual participant’s score. Label each part of the graph. Vertical (y-axis, DV) Horizontal (x-axis, IV) Bar-graphs are good to compare categories. Histograms are good for a continuous IV. F. Results: Inferential Statistics Even if you suffer from calculatophobia (fear of statistics), you must include inferential statistical analysis of your results in this section. You probably have some idea of the results that you wanted from your experiment. Do not try to be a cheerleader rooting for your specific results. In research, you want to be a non-partisan (not taking sides) bystander. Don’t be so in love with your outcome that you lose your professional objectivity and become biased in your evaluation of the data. You must interpret the data regardless of the outcome—even if you failed to show a significant change between rock music and classical music (for example). Inferential statistics ask if your random sample really represents the whole population. Can we infer from the small group how the large group would behave? Example: Could I ask 10% of the class to place a vote on something, and then apply that vote to the entire class? Could 1 handful of beans tell me the health and taste of the entire bin of beans? If 10 states voted for one presidential candidate, could we infer that the rest of the country will likely vote the same way? Is it likely that the math anxiety experienced by economics majors could be applied to the entire population of people majoring in economics that could have been sampled? The Null Hypothesis (N0) The Null Hypothesis (N0) assumes that there is no significant difference in your data. In statistics, The Null is assumed to be true…your experiment is either proving the Null to be right or wrong. Think of this like a court of law where you are assumed innocent until proven guilty.