Macquarie University Assignment
Science Literacy Assessment 4 - Handling Data
Data, data, data…. That is what science is really about in the end. Regardless if the experiment requires recording behavioural videos, timing responses, blood samples taken, counting number of sperm, or measuring fertilised eggs. All the hours collecting samples, can then require hours more processing the samples collected to turn them into numbers that can be statistically analysed and turned often into figures. For example, behavioural videos have to be watched and scored and translated into numbers, and blood samples can be used for genetic work, hormone analysis, or to count parasites.
Goal of Assessment: In this assessment you will learn how to select and organise the right part of a basic data and how to handle it to help you make statements about the research findings and to make figure. You will also learn about the interpretation of statistics.
The Assessment: You will be provided with a small data set that you will have to answer questions about.
Handling data tutorial: Using excel and interpreting the data
When you collect data from an experiment you often collect information about multiple variables that are categorized, some time in multiple ways. This means that you will have multiple columns of data that contain a category or numbers. The categories can help you group the numbers for analysis.
Watch the video on ECHO for a demonstration of how to use excel <NOW AVAILABLE>.
You will learn:
We are not asking you to run any statistical tests in the course. You just need to be able to interpret the outputs that we give you.
What statistical tests you run depend on what kind of data you have and the distribution. For example, if you have measures or value data (e.g. weight of egg) vs. count data (e.g. number of eggs) In statistical terms this means you have continuous (i.e. can take on any value) vs. a discrete (i.e. whole number or category) data set.
The statistics that will be presented to you for the posters will be simple ANOVAs or t-tests. Both tests essentially are looking to test fro differences between groups. The outputs will give you information that needs to be reported in your Results section.
What you need to report:
T-Tests – you need to report the t-stat number, the degrees of freedom, and the p-value. e.g. t = -1.593, df = 12, p = 0.297
ANOVA – you will get an F statistic, degrees of freedom in group sand individuals, and a p-value. e.g. F3,31.2 = 0.47, p = 0.73
BUT, what does it mean!!
The test you run on the data puts different constraints on the data, but with a-priori (before you run the test based on assumptions you make before running the experiment). The classic way to judge if groups significantly differ is the p-value. If the p-value is p < 0.05 then the groups are considered to be significantly different. Typically a p = 0.001 represents a stronger difference between groups then p =0.04. By stronger, I mean it is a result more likely to stand up to further testing.
Science is generally moving away from the p-value though, so don’t be surprised if you see significance presented in papers in different ways – especially more recent papers. However, for this course you don’t need to worry about this!.
Use the following data set (as xlsx as doc) answer the following questions, put them into a document for submission into turnitin.
To be completed in a document and submitted to TurnItIn - you only get one upload.
Please include you Name, Student ID number, and number your answers as they are asked below to avoid confusion with marking.
You do not need to include a title page with your name and ID. Please use a legible font and text size. Normal spacing is fine. Please save file with at least sir name, your student number, and assessment #.