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.

Stat Test Choice.pdf

All Hypothesis Tests Flow Chart

Large flow chart of hypothesis tests, includes null & alternative hypotheses, regular & non-parametric tests

Streamlined Flow Chart.pdf

Streamlined Hypothesis Tests Flow Chart

Streamlined flow chart of hypothesis tests common in Biology IAs

1 sample t-test.mp4

1-Sample t-Test

1 Categorical explanatory variable (1 treatment) , quantitative, continuous response variable

paired t-test.mp4

Paired t-Test

Each subject gets 2 treatments , quantitative, Continuous response variable

2-sample t-test.mp4

2-Sample t-Test

2 treatments (qualitative explanatory variables) tested with quantitative continuous response

Chi-Squared GOF.mp4

Chi-Squared Goodness of Fit (1 variable)

1 Categorical explanatory variable, response is categorical

Chi-Squared Ind or Homogeneity.mp4

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.mp4

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)

Anova 2-way.mp4

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.pdf

Normal Table

Full Normal table for finding Critical Values, p-values, probabilities, and cut-offs

t Table.pdf

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.pdf

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.pdf

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