Biology IA Help
Follow Dr. Akinyode's 12-Step Experiment Plan to ensure you design and carry out a thorough investigation that can be analyzed to reveal the most results.
Some sources to assist you in that process are included here. I made video explanations for t-tests and ANOVA. For 1-way and 2-way ANOVA, the videos here provide a good explanation for both independent and repeated measures (the only difference between them is you don't check independence for repeated measures and you'll use the specific online calculator for repeated measures 1-way or 2-way ANOVA).
In addition, a link to an EXTREMELY HELPFUL website is included here and on the Psychology IA Help page. The website contains all of the non-parametric tests I mention that you need to use if your conditions are not true. You may notice a little difference in the vocabulary around types of data. Psychology divides Categorical/Qualitative data into Nominal (named variables, like hair color) and Ordinal (scaled variables, like satisfaction level) data. They also divide Quantitative data into Interval (arbitrary zero, like bank account balance) and Ratio (zero means none, like height) data. But the website is so helpful, it precluded my needing to duplicate what was there.
The 'Statistics Info Sheets Page' features one-page info sheets explaining many common tests.
The 'Statistics Video Page' features broad overview videos for earlier in your process when you're planning out your data collection & analysis.
All Hypothesis Tests Flow Chart
Large flow chart of hypothesis tests, includes null & alternative hypotheses, regular & non-parametric tests
Streamlined Hypothesis Tests Flow Chart
Streamlined flow chart of hypothesis tests common in Biology IAs
1-Sample t-Test
1 Categorical explanatory variable (1 treatment) , quantitative, continuous response variable
Paired t-Test
Each subject gets 2 treatments , quantitative, Continuous response variable
2-Sample t-Test
2 treatments (qualitative explanatory variables) tested with quantitative continuous response
Chi-Squared Goodness of Fit (1 variable)
1 Categorical explanatory variable, response is categorical
Chi-Squared for 2-way Data
1 population, 2 Categorical explanatory variable, responses are categorical
2 populations, 1 categorical explanatory variable, responses are categorical
1-Way ANOVA
>2 treatments (qualitative explanatory variables) tested with quantitative continuous response variable
(for repeated measures eliminate Independence condition & use the 1-way ANOVA Repeated Measures calculator)
2-Way ANOVA
>2 variables (each with multiple levels) tested with quantitative continuous response variable
AMAZING SOCIAL SCIENCE WEBSITE
Provides statistical test calculators and explanations, draws histograms & bar charts, slightly different terminology
AMAZING CALCULATOR WEBSITE
Provides statistical test calculators and explanations, draws graphs, randomizes data, calculates binomial probability
Normal Table
Full Normal table for finding Critical Values, p-values, probabilities, and cut-offs
t-Table
Full t-table for finding Critical Values, p-values, probabilities, and cut-offs
For more-precise p-values, use one of the online calculators or a TI84 / TInspire
For the Critical Value for a lower-tail test, add a negative sign to the value from the chart
Chi-Squared Table
Full Chi-Squared table for finding Critical Values, p-values, probabilities, and cut-offs
For more-precise p-values, use one of the online calculators or a TI84 / TInspire
df = #categories-1 for a Goodness of Fit Test
df = (#rows-1)(#columns-1) for a test of Independence or Homogeneity
F Table
Full F table for finding Critical Values, p-values, probabilities, and cut-offs
For more-precise p-values, use one of the online calculators or a TI84 / TInspire
df1 = numerator degrees f freedom
df2 = denominator degrees of freedom