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​The data

data collection - 
data processing/analysis - 

You are required to complete statistical measures. At this level of research, it 
is simply not an option. The good news is that there is help for you. Below is a 
Brief Course in Statistics written by Dru Martin at Ramstein High School. This 
will assist you with your data analysis. 

Data gathering + measurement: 
Ultimately the analysis you can do, the graphs you can make and the conclusions 
you can come to depend on the data that you gather. The richer in information 
your measurements are, the better the rest of your project can be. To 
illustrate:

1.    
The bacteria grew yes/no 



2.    
The bacteria grew (circle one) none  some  lots  



3.    
The bacteria covered ____% of the surface. 



Number 
one is an example of a categorical measurement; your observation fits into one 
of two categories. Number two is also a categorical measurement, but the 
categories have an inherent order. Number three is a quantitative measurement. 
As we move from 1 to 2 to 3, the amount of information contained in the data 
increases, and the analysis possibilities increase as well. When possible, take 
quantitative measurements of your dependent and independent 
variables.

Graphing data: Your experiment will most likely be comparing a 
control setup with an experimental setup. Therefore, your graphs should clearly 
compare these two results, preferably on the same graph.

The type of graph you can draw depends on the type of data that you took. For
categorical data, a pie chart or histogram is best. For one quantitative measurement,
a bar graph, boxplot, stem-and-leaf diagram or dotplot are all good alternatives. If 
you have a quantitative measurement for each of your experimental groups, you 
will need to display all the groups on one graph. Finally, if you are looking at 
the relationship between two quantitative measurements, a scatterplot is most 
useful, and a least squares regression line may be 
appropriate.

Graphing 
data: Your experiment will most likely be comparing a control setup with an 
experimental setup. Therefore, your graphs should clearly compare these two 
results, preferably on the same graph.
The 
type of graph you can draw depends on the type of data that you took. For 
categorical data, a pie chart or histogram is best. For one quantitative 
measurement, a bar graph, boxplot, stem-and-leaf diagram or dotplot are all good 
alternatives. If you have a quantitative measurement for each of your 
experimental groups, you will need to display all the groups on one graph. 
Finally, if you are looking at the relationship between two quantitative 
measurements, a scatterplot is most useful, and a least squares regression line 
may be appropriate.

Video - Introduction to Statistical Analysis

OPTIONS FOR STATISTICAL ANALYSIS
T-Test PowerPoint Explanation

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Copyright © 2015
  • JSHS Europe
    • JSHS Information
  • JSHS Projects
    • Project Topic
    • Researching your topic
    • Design your Research Method
    • Data Collection and Processing
    • Write Your Paper
    • Turn In Your Paper
    • Teacher/Mentor Page
  • JSHS Starters
  • 2020
    • 2020 Live Stream
    • Oral Presentations 2020
    • Poster Presentations 2020
    • 2020 Key Note
    • 2020 Awards
  • 2019
    • Event / Promo Video 2019
    • JSHS 2019 - Awards
    • Oral Presentations 2019
    • Poster Presentations 2019
    • Evaluations
  • 2018
    • JSHS 2018 - Awards
    • Oral Presentations 2018
    • JSHS National 2018
    • Oral Presentations 2018
    • Poster Presentations 2018
    • Event / Promo Video 2018
    • Photo Gallery 2018
  • 2017
    • JSHS National 2017
    • Oral Presentations 2017
    • Poster Presentations 2017
    • Event / Promo Video 2017
    • Photo Gallery 2017
  • 2016
    • JSHS National 2016
    • Oral Presentations 2016
    • Poster Presentations 2016
    • Event / Promo Video 2016
    • Photo Gallery 2016
  • 2015
    • 2015 Regional Broadcast
    • Oral Presentations
    • Photo Gallery 2015
    • JSHS National 2015
    • MORE >
      • Poster Presentations
      • Awards
      • Challenge Competition
      • Event Videos
      • Keynote Speakers
      • Poster Presentations
      • Award Ceremony
      • Awards
      • Poster Presentations
      • Event Videos
      • Challenge Competition
      • Keynote Speakers
  • 2014
    • Oral Presentations
  • Contact Us