Biology IA Data Processing

[Iohannes]

Hi,

My research question is "what are the effects of gravity on directional plant growth and development in small white kidney beans?". I've carried out my experiment and collected my data, with 10 trials for each of the 3 independent variables: bean seed's hilum facing 3/9 o'clock, 6 c'clock, and 12 c'clock. Aside from some raw qualitative data and the measured lengths of the bean plants (collected after 1 and a half week), I do not know if my data processing needs to have more than just the mean and standard deviation for each of the 3 independent variables. My teacher suggested me to graph the lengths of the bean plants and base my discussion off of it, but she didn't say anything about incorporating other statistical analysis such as 2-tailed t tests or normal distribution. Any feedback would be appreciated.

Thanks,

Iohannes

[IB`NOT`ez]

Statistical tests should only be added when they're necessary, not just for the sake of having one.

Use your Standard Deviation values to plot error bars for your graph, and then you can discuss whether your linear graph passes through all the error bars – if not, then your results are imprecise, with a significant degree of random errors.

Uncertainties also aren't something to be discounted just because it's biology – if you used any tools such as rulers, you could calculate the percentage uncertainties for each of your mean values of each Independent Variable, giving you a little something extra to discuss. 

Mean, Standard Deviation, Error Bars, Graphs, Pearson's Correlation Coefficient, Percentage Uncertainties – all those are enough to net you high marks for the Data Processing criteria. To achieve full marks in that criteria though, what you actually need to do is relate all your uncertainties to significance – whether it be measurement errors, methodological errors etc. 

Good luck!