LabWriteUp is a report writing application for students and researchers in science
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Results
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Results
The results component of LabWriteUp can help you write your results section. It will not carry out an analysis for you but assumes that you have already carried your analyses before using this component. However, it will allow you to plot graphs and create tables to convey your data. The first step is to select the particular test that you used on your data. The information you need to enter is different depending upon the test. LabWriteUp will begin with a statement about the precise nature of the test that you carried out. The level of significance. The p value obtained from the analysis can be entered as the actual computed probability or if you calculated the stats by hand, the level of significance found from the statistical table can be entered by selecting one of the buttons corresponding to a level of significance. When you enter an obtained value LabWriteUp will also calculate the appropriate level of significance (so if you enter .0048, LabWriteUp will treat the result as p < .005, or if you enter .273 LabWriteUp will treat it as p > .05). Alpha. The probability value obtained in an analysis is usually compared to an alpha value of .05. Anything below that is judged as statistically significant. Alpha can sometimes be set to a lower value, depending on certain features of a design (such as the number of comparisons being made). In LabWriteUp alpha is fixed at 0.05. If you are using a different value then you will need to edit the results output accordingly. The tests that LabWriteUp can help you to present in your report are shown below. Click on one of the tests for help on that test. |
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| Wilcoxon signed ranks test | Mann-Whitney U test | independent t test | ||
| paired t test | Pearson correlation | Spearman correlation | ||
| One way repeated measures analysis of variance (ANOVA) | One way independent measures ANOVA | Two way repeated measures ANOVA | ||
| Two way repeated measures ANOVA | Factorial ANOVA | Multiple regression | ||
| One variable chi-square test | 2 x 2 chi-square test | r x c chi-square test | ||
The type of test chosen depends on the type of data obtained in the study, the type of design, whether the data collected violates the assumptions for a parametric test or not, and whether the hypothesis predicts a difference or an association.