Zebrafish Research Data Analysis Choices

Data Analysis Statistical Analysis Dealing With Population Frequency and Proportions (Percentages) Two-proportion z-test – comparison of two population proportions (percentages) Calculator - http://www.socscistatistics.com/tests/ztest/ Example

Data Analysis Statistical Analysis Dealing With Population Frequency and Proportions (Percentages) Comparison of multiple population proportions (percentages) – a two-step process Step 1: Contingency tables, chi-square approach - Use Chi-Square (χ2) analysis of contingency tables to compare the numbers of individuals of two or more groups that fall into two or more different categories. See 7.3 Chi-square Analysis of Contingency Tables for more information. This step will tell you if there are significant differences among the various population proportions but not between specific pairs of proportions Chi-square contingency tables calculator http://graphpad.com/quickcalcs/chisquared1.cfm

Data Analysis Statistical Analysis Dealing With Population Frequency and Proportions (Percentages) Comparison of multiple population proportions (percentages) – a two-step process Step 2: Marascuilo's post hoc analysis following the chi-square test The Marascuilo procedure allows for the simultaneous testing the differences between all pairs of proportions when there are several populations under investigation (more than two). Marascuilo's Post Hoc Analysis description http://itl.nist.gov/div898/handbook/prc/section4/prc474.htm

Z Test Calculator for Multiple Proportions

Data Analysis Statistical Analysis Dealing With Two or more data sets (non-percentages/proportions) T - test – compares the means of two groups of numerical values (not proportions/percentages). Calculator - http://www.graphpad.com/quickcalcs/ttest1.cfm Example

Data Analysis Statistical Analysis Dealing With Two or more data sets (non-percentages/proportions) Step 1 ANOVA - compares three or more group means to determine if there is a significance among group means Calculator - http://danielsoper.com/statcalc3/calc.aspx?id=43 Step 2 – a post hoc test must then be run to determine the differences between all pairs of means (Tukey test) Calculator http://faculty.vassar.edu/lowry/hsd.html - four means http://graphpad.com/quickcalcs/posttest1.cfm - multiple means

Data Analysis Statistical Analysis Dealing With Two or more data sets (non-percentages/proportions) ANOVA + Tukey post hoc test- compares three or more group means, a two step process http://faculty.vassar.edu/lowry/hsd.html - calculator - four means Example

Data Analysis Standard error of the mean The Standard Error is the term used in statistics to refer to an estimate of that standard deviation, derived from a particular sample used to compute the estimate. Represented as ±SEM Every statistic has a standard error associated with it. A measure of the accuracy of the statistic can be derived that the standard error of 0 represents that the statistic has no random error and the bigger represents less accuracy of the statistics. SEM calculator http://ncalculators.com/statistics/standard-error- calculator.htm

Standard Deviation Calculator http://www.calculator.net/standard-deviation-calculator.html For Z test https://www.easycalculation.com/standard-deviation-of-percentages.php

Data Analysis How to place error bars on figures using Excel Tutorial http://www.extendoffice.com/documents/excel/863-excel-add-remove-error-bars.html

Data Analysis Statement Writing a data analysis section for material and methods analyzing population proportions/percentages and numerical values (example): Statistical analysis was performed using SigmaStat 3.5 software, Microsoft Excel, and VasserStats analysis website. Heart rate and blood vessel data was entered into either an ANOVA one-way paired t-test or ANOVA Single Factor tests. For the ANOVA analysis a Holm–Sidak post hoc method was also run to determine significant differences between the various treatment groups. Percent survival and arrhythmic heart rate data were analyzed using either by a z-test for two population proportions or for multiple proportions using a chi-square contingency table test, followed by a Marascuilo’s post hoc analysis. Sample sizes for all n = 30. All experiments were repeated in triplicate.