Assessment 1: Scientific writing & t-tests (25%)
Assessment 1: Scientific writing & t-tests (25%)
Due date: Week 5. 1159pm Friday 29th March 2024 (submit via turnitin)
This assignment is to be completed individually. It is good learning practice to discuss general concepts with other students and work together to improve your understanding of unit content. However, it is NOT acceptable to work closely with other students to complete the actual assignment questions, or collaborate on the written presentation of this assignment.
This assessment has been designed to give you the opportunity to practice and learn concepts in a real-life way. These concepts will be essential for you to improve your academic writing as you move forward in your careers. Make the most of this opportunity by working independently! You will need to apply these concepts in real life, so if you find any aspects of this assignment difficult, it is better to ask for help from your demonstrator or lecturer than to copy from another student.
Academic integrity
students.curtin.edu.au/essentials/rights/academic-integrity/Academic integrity at its core is about honesty and responsibility and is fundamental to Curtins expectations of you. This means that all of your work at Curtin should be your own and it should be underpinned by integrity, which means to act ethically, honestly and with fairness.
As a Curtin student you are part of an academic community and you are asked to uphold the UniversitysCode of Conduct, principles of academic integrity, andCurtins five core valuesof integrity, respect, courage, excellence and impact during your studies.
You arealso expected to uphold theStudent Charterand recognise that cheating, plagiarism collusion, and falsification of data and other forms of academic dishonesty are not acceptable.
Please carefully read the following information regarding appropriately acknowledging your internet resources
Use of internet sources; Generative AI, forums, and online blogs
In this unit you are allowed to use internet sources such as forums (e.g. Stack overflow), blogs and generative AI (e.g. ChatGPT) as a resource to help with your coding and R scripts. Please note however that this assignment does not require the use of R or R Studio, you are simply interpreting the code.
You should not use these sources to help with your written work and interpretation.
If you use a resource other than those provided by us this should be acknowledged in writing. You should include a declaration, in your code, or in an acknowledgements section for a scientific report.
The declaration should be similar to this:
I acknowledge the use of (insert resource name and URL) in the preparation of my code and statistical analysis. I have used (insert resource name) to assist with (specify the steps where the resource has been used).
Please review the library guidelines on referencing:
Referencing generative AI (e.g., ChatGPT)
Updated guidelines have been incorporated into the four referencing guides, following the approach adopted by Curtin for semester one. The guides outline the requirement to use in-text citations (following personal communication reference types) where text has been quoted or paraphrased, and the inclusion of a declaration, detailing how generative AI has been used in the assignment process.
Please carefully read the following information regarding academic misconduct; cheating, collusion, and plagiarism
What is academic misconduct?
Academic misconduct refers to conduct by a student that is dishonest or unfair in connection with any academic work.
Cheating is acting dishonestly or unfairly in order to gain an advantage. Examples include:
Cheating in an exam, test or supervised assessment activity.
Cheating in an assessment or other assessable work:
Submitting written or creative work which has been drafted or produced by someone else including friends, family or a paid contracting service (this is known as contract cheating, assessment outsourcing or ghost writing) and claiming authorship for it. This includes:
Allowing someone or an organisation to draft or complete an assessment task on your behalf
Contracting another person to do the work for you
Purchasing work from another source
Allowing or contracting another person to edit and substantially change your work
Collusion is where students act together in relation to the preparation or presentation of any assessed item of work in a manner that is dishonest or unfair. Examples include:
Working with another person (colluding) when the assessment should be completed individually;
In the case of collaborative group projects, falsely representing the individual contributions of the collaborating group members.
Plagiarism is knowingly presenting the work or property of another person as your own without appropriate acknowledgement or referencing. It includes:
Copying of sentences, paragraphs or creative products (in whole or in part) which are the work of other persons without due acknowledgment. Creative products include webpages, books, articles, theses, unpublished works, working papers, seminar and conference papers, internal reports, lecture notes or recordings, computer files, images or video
Too closely paraphrasing sentences, paragraphs or themes without due acknowledgment
Using another persons work (including words, music, creative or visual artefacts, computer source code, designs, problem solutions or ideas)
In the case of collaborative group projects, falsely representing the individual contributions of the collaborating partners
Submitting work which has been produced by someone else including friends, family or a paid contracting service (This is known as contract cheating, assessment outsourcing or ghost writing.)
Submitting ones own previously assessed or published work for assessment or publication elsewhere, without appropriate acknowledgement (self-plagiarism)
Allegations of misconduct are managed in accordance withStatute 10: Student Discipline. Thestudent conduct guidehelps explain the process if you receive an allegation of misconduct.
Refer to https://about.curtin.edu.au/governance/academic-integrity/ and the student conduct guide for more information, including student guidelines for avoiding plagiarism.
This assessment is worth 25% of your final mark and must be submitted through Turnitin. It will be fully introduced during the computer lab in week 2.
This assignment assesses the following learning outcomes:
Write scientific methods with appropriate subheadings.
Select and justify appropriate methods of statistical analysis for data and interpret and describe the output of these in a manner appropriate for a scientific report.
Appropriately formulate and present results (i.e., present results and figures fit for a scientific report)
Annotate and understand R code outputs
Assessment instructions:
You are part of a marine research group at Curtin University. Your team has been conducting surveys measuring coral cover (%) in Coral Bay annually since 2015. The surveys occur in September of each year across multiple sites. At one of these sites, Bills Bay (23.14S, 113.76E), there was an anoxic event that occurred in March 2022, resulting in mass fish kills. However, the impact this event had on the coral in the region is not known. You have been tasked by the Department of Primary Industries and Regional Development (DPIRD) to compare mean coral cover in Bills Bay at the same site (Site A) between the years 2021 and 2022, and produce a report of your findings.
The transects conducted at the study site in Bills Bay are permanent fixed transects. These transects are GPS recorded and the same transects are revisited every year in September. Ten replicate 10 x 2 m (i.e., 20 m2) transects are surveyed via snorkel, with each transect surveyed parallel to the coastline and separated by 5 m. The percentage of coral cover is recorded through point intercept method, whereby live coral cover is recorded every 20 cm along the transect (50 per transect). These values are then summed and a percentage of live coral cover calculated for each transect (see Table 1).
Table SEQ Table * ARABIC 1. The percentage of live coral cover measured for each transect in 2021 (before the anoxic event) and 2022 (after the anoxic event)
Site Transect Year Mean live coral cover (%)
Bills Bay 1 2021 79
Bills Bay 2 2021 62
Bills Bay 3 2021 56
Bills Bay 4 2021 72
Bills Bay 5 2021 43
Bills Bay 6 2021 62
Bills Bay 7 2021 68
Bills Bay 8 2021 82
Bills Bay 9 2021 89
Bills Bay 10 2021 54
Bills Bay 1 2022 31
Bills Bay 2 2022 50
Bills Bay 3 2022 36
Bills Bay 4 2022 45
Bills Bay 5 2022 51
Bills Bay 6 2022 43
Bills Bay 7 2022 49
Bills Bay 8 2022 42
Bills Bay 9 2022 36
Bills Bay 10 2022 48
Your Task
You are to write up methods and results for your experiment in the format of a scientific paper/report. Please carefully read the instructions on the following pages and the rubric to ensure that you complete the assessment appropriately. Please use size 12 font and normal page margins in your assignments. "A Guide to Scientific Writing", available on BlackBoard, can assist in structuring your assignment, answering the questions, and developing your scientific writing.
Your paper should include the following sections:
1) An alternate (HA) hypothesis, no null hypothesis is necessary. When creating an alternate hypothesis, make a prediction instead of just stating that there will be a significant difference. For instance, you could predict which year will have a higher or lower mean coral cover. Hint: remember to include all the relevant details to your study!
04372744Figure SEQ Figure * ARABIC 1: Map of study region Bills Bay located in Coral Bay (Western Australia). The inset indicates both Bills Bay (red square) and Perth (black square). The Bills Bay study site (A) was approximately 100m offshore. Ten replicate 10 x 2 m belt transects were conducted at the same site in both 2021 and 2022.
00Figure SEQ Figure * ARABIC 1: Map of study region Bills Bay located in Coral Bay (Western Australia). The inset indicates both Bills Bay (red square) and Perth (black square). The Bills Bay study site (A) was approximately 100m offshore. Ten replicate 10 x 2 m belt transects were conducted at the same site in both 2021 and 2022.
1147011744855002) Methods: a detailed and concise methods section with appropriate subheadings. Your methods should describe your site/location of the study, experimental design and sampling, and the statistical analyses conducted (i.e. name the test, why its an appropriate choice, assumptions). You may use the following map in your site description.
3) Results: A results section with appropriate tables and graphs. Please follow the structure we have used in class, and in your lecture examples. You'll find the necessary R code for your results section on Blackboard, and at the end of this assignment sheet. There are 2 code appendices provided to you- you must use your judgement to pick ONE that is appropriate for the data you have. When presenting your data you must have at least one figure, ensuring that you present and structure it/them appropriately. It is recommended that you use an appropriate plot to illustrate your means. For guidance on figures and captions, please refer to the example map provided above, the video on blackboard, and the examples from the workshop.
4) Questions: No introduction or discussion is necessary for this task, but you will need to answer the following questions to prepare for the next assignment and develop your scientific writing skills. Include a "Questions" section in your assignment and use A Guide to Scientific Writing to address the following:
How many ideas should be addressed in one discussion paragraph? (1 MARK)
What are the three (3) components that form a discussion paragraph? (1 MARK)
How should you structure (balance) your arguments/paragraphs in your discussion? (1 MARK)
Why are citations crucial in discussions? (1 MARK)
In a discussion, you should compare your results to the published literature. What does the statement compare your results to the literature mean and how does it differ from writing an introduction? You do not need A guide to Scientific writing for this question (2 MARKS)
5) References: You must reference any information you may have used for your site description or other sections. Please use APA formatting, see https://uniskills.library.curtin.edu.au/referencing/apa7/introduction/. If you are unfamiliar with referencing, you can try using a software like EndNote / Zotero / Mendeley to help you get started.
6) Appendix: You need to comprehensively annotate the R code provided to you for your results, and include both the R code and the annotations as an appendix. It's crucial to provide detailed annotations for the code, explaining the function and resulting outputs, just as we do in your lab classes. There are two code appendices provided to you- you must use your judgement to pick ONE that is appropriate for the data you have. When adding your annotations, please use a different coloured font or bold text.
See example:
#Create a qqplot to check normality visually
qqnorm(ExampleData$Column)
#Data looks normal, some deviations on the positive side but should be okay. Will check with shapiro-wilk test
Code for analysis 1:
setwd(/Users/Student/Documents/QuantitativeBiology/Assignment1)
library(car)
library(doBy)
CoralCover<-read.csv("ttest_Data.csv")
str(CoralCover)
# 'data.frame':20 obs. of 4 variables:
# $ Site : chr "Bills Bay" "Bills Bay" "Bills Bay"...
# $ Transect : int 1 2 3 4 5 6 7 8 9 10 ...
# $ Year : int 2021 2021 2021 2021 2021 2021 2021 2021...
# $ MeanCoralCover: int 79 62 56 72 43 62 68 82 89 54 ...
CoralCover$Year<-as.factor(CoralCover$Year)
str(CoralCover)
# 'data.frame':20 obs. of 4 variables:
# $ Site : chr "Bills Bay" "Bills Bay" "Bills Bay"...
# $ Transect : int 1 2 3 4 5 6 7 8 9 10 ...
# $ Year : Factor w/ 2 levels "2021","2022": 1 1 1 1 1 1 ...
# $ MeanCoralCover: int 79 62 56 72 43 62 68 82 89 54 ...
par(mfrow=c(2,1))
hist(CoralCover$MeanCoralCover[CoralCover$Year=="2021"])
hist(CoralCover$MeanCoralCover[CoralCover$Year=="2022"])
qqnorm(CoralCover$MeanCoralCover[CoralCover$Year=="2021"])
qqline(CoralCover$MeanCoralCover[CoralCover$Year=="2021"])
qqnorm(CoralCover$MeanCoralCover[CoralCover$Year=="2022"])
qqline(CoralCover$MeanCoralCover[CoralCover$Year=="2022"])
tapply(CoralCover$MeanCoralCover, CoralCover$Year, shapiro.test)
# $`2021`
#
# Shapiro-Wilk normality test
#
# data: X[[i]]
# W = 0.98325, p-value = 0.9802
#
#
# $`2022`
#
# Shapiro-Wilk normality test
#
# data: X[[i]]
# W = 0.92049, p-value = 0.361
par(mfrow=c(1,1))
-4010523725600boxplot(CoralCover$MeanCoralCover~CoralCover$Year)
leveneTest(CoralCover$MeanCoralCover~CoralCover$Year)
# Levene's Test for Homogeneity of Variance (center = median)
# Df F value Pr(>F)
# group 1 4.5013 0.04801 *
# 18
# ---
# Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
t.test(CoralCover$MeanCoralCover~CoralCover$Year, mu=0, alt="two.sided", conf=0.95, var.eq=T, paired=F)
# Two Sample t-test
#
# data: CoralCover$MeanCoralCover by CoralCover$Year# t = 4.7571, df = 18, p-value = 0.0001575
# alternative hypothesis: true difference in means between group 2021 and group 2022 is not equal to 0
# 95 percent confidence interval:
# 13.17741 34.02259
# sample estimates:
# mean in group 2021 mean in group 2022
# 66.7 43.1
MeansSEtable<-summaryBy(MeanCoralCover~Year, data=CoralCover,FUN=function(x) + {c(mean=mean(x),sd=sd(x),num=length(x), se=sd(x)/sqrt(length(x)))})
MeansSEtable# Year MeanCoralCover.mean MeanCoralCover.sd MeanCoralCover.num MeanCoralCover.se
# 1 2021 66.7 14.118939 10 4.464801
# 2 2022 43.1 6.838616 10 2.162560
Code for analysis 2:
setwd(/Users/Student/Documents/QuantitativeBiology/Assignment1)
library(car)
library(doBy)
CoralCover<-read.csv("ttest_Data.csv")
str(CoralCover)
# 'data.frame':20 obs. of 4 variables:
# $ Site : chr "Bills Bay" "Bills Bay" "Bills Bay"...
# $ Transect : int 1 2 3 4 5 6 7 8 9 10 ...
# $ Year : int 2021 2021 2021 2021 2021 2021 2021 2021...
# $ MeanCoralCover: int 79 62 56 72 43 62 68 82 89 54 ...
CoralCover$Year<-as.factor(CoralCover$Year)
str(CoralCover)
# 'data.frame':20 obs. of 4 variables:
# $ Site : chr "Bills Bay" "Bills Bay" "Bills Bay"...
# $ Transect : int 1 2 3 4 5 6 7 8 9 10 ...
# $ Year : Factor w/ 2 levels "2021","2022": 1 1 1 1 1 1 ...
# $ MeanCoralCover: int 79 62 56 72 43 62 68 82 89 54 ...
par(mfrow=c(2,1))
hist(CoralCover$MeanCoralCover[CoralCover$Year=="2021"])
hist(CoralCover$MeanCoralCover[CoralCover$Year=="2022"])
qqnorm(CoralCover$MeanCoralCover[CoralCover$Year=="2021"])
qqline(CoralCover$MeanCoralCover[CoralCover$Year=="2021"])
qqnorm(CoralCover$MeanCoralCover[CoralCover$Year=="2022"])
qqline(CoralCover$MeanCoralCover[CoralCover$Year=="2022"])
tapply(CoralCover$MeanCoralCover, CoralCover$Year, shapiro.test)
# $`2021`
#
# Shapiro-Wilk normality test
#
# data: X[[i]]
# W = 0.98325, p-value = 0.9802
#
#
# $`2022`
#
# Shapiro-Wilk normality test
#
# data: X[[i]]
# W = 0.92049, p-value = 0.361
t.test(CoralCover$MeanCoralCover~CoralCover$Year mu=0, alt="two.sided", conf=0.95, paired=T)
# Paired t-test
#
# data: CoralCover$MeanCoralCover by CoralCover$Year# t = 3.9343, df = 9, p-value = 0.003436
# alternative hypothesis: true mean difference is not equal to 0
# 95 percent confidence interval:
# 10.03041 37.16959
# sample estimates:
# mean difference
# 23.6
MeansSEtable<-summaryBy(MeanCoralCover~Year, data=CoralCover,FUN=function(x) + {c(mean=mean(x),sd=sd(x),num=length(x), se=sd(x)/sqrt(length(x)))})
MeansSEtable# Year MeanCoralCover.mean MeanCoralCover.sd MeanCoralCover.num MeanCoralCover.se
# 1 2021 66.7 14.118939 10 4.464801
# 2 2022 43.1 6.838616 10 2.162560
Assignment rubric
Assessment component Needs improving Acceptable Good Excellent
Hypothesis (max 4) No hypothesis provided Hypothesis provided but needs major improvements Hypothesis provided; acceptable but could be improved Hypothesis contains the required elements and is relevant to the experiment
0 1 2 3-4
Methods (max 12) Missing several details in site description, experimental design and statistical analysis Site description provided A good site description A detailed and appropriate site description with relevant information
Inappropriate experimental design, missing several key details and/or not replicable Some details on experimental design and some steps taken are inappropriate and/or lacking Most details included for experimental design are appropriate and relevant Experimental design includes all information necessary to be replicable by reader. Clear and comprehensive
Inappropriate data collection/sampling details Some sampling steps taken are inappropriate and/or lacking Some sampling information may be missing, not clearly written, or irrelevant information included Provides a clear and comprehensive account of methods used in collecting the data
Writing style inconsistent, bullet points used, incorect use of tenses, flow needs addressing Writing style inconsistent, or bullet points used, or incorect use of tenses, or flow needs addressing Written in the past tense using a non-personalised format. May need to work on concise scientific writing style or flow Provides a clear and comprehensive account of methods used in collecting the data. Written concisely and scientifically with correct use of tenses.
Incorrect details included in statistical data analysis, incorrect choice of t-test, or raw data included Incomplete details on statistical analysis. May have incorect choice of t-test and/or included raw data. Statistical analysis section includes an appropriate choice of data analysis and comprehensive justification of this decision, considering data assumptions and experimental design. Some details missing. A statistical analysis section that includes an appropriate choice of data analysis and comprehensive justification of this decision, considering data assumptions and experimental design.
<4 4-6 6-9 9-12
Results & Figures (max 16) Hypotheses not addressed. All hypotheses addressed. All hypotheses addressed. All hypotheses addressed.
Results are not presented in an organised format. Flow or information confusing Some discussion of results. Some description of the data, but could do with major improvements Purely descriptive no interpretation or discussion of results. May not be written concisely, flow needs improving, or does not refer to figures. Purely descriptive no interpretation or discussion of results. A concise and logical description of the key trends with reference to tables and figures. Logical flow of information.
Statistical results incomplete, incorrect choice of t-test. No description of the data Results missing a few statistical numbers, but mostly complete. May have chosen incorrect t-test Correct choice of t-test. Almost all p values, test statistics, sample sizes, alpha levels and means/other relevant values appropriately quoted. Correct choice of t-test. All tests specified, p values, test statistics, sample sizes, alpha levels and means/other relevant values appropriately quoted.
Poor choice of data representation and/or no figures included. Tables and growth provided, but some element incomplete or inaccurate. Figure may not be integrated with text. Tables and graphs with correct units and captions, some formatting issues. Figure integrated with text. Tables and graphs with correct units and captions. Good choice of graphs / tables to illustrate trends in data. Figure integrated with text.
No captions or inappropriate captions. Caption not clear or concise, missing some detail to be 'stand alone' Clear and concise captions, missing some detail to be 'stand alone' Clear and concise captions, enough detail to be 'stand alone', correct placement.
<4 4-8 8-12 12-16
Questions (max 6) Not all questions addressed and/or explanations do not address the question asked. All questions addressed, explanations may not be clear or appropriately address the question. All questions addressed, explanations clear and well thought out. All questions addressed correctly, explanations clear and well thought out.
<3 3-4 4-5 5-6
References (max 5) Uses 1 or no references Uses between 1-2 references Uses between 2-3 references Uses at least 3 references
Poor reference choice OK reference choice (mix of tertiary and less appropriate references) Uses references appropriate for tertiary level Uses references appropriate for tertiary level
References not formatted in a consistent style Reference formatting consistent Reference formatting consistent Reference formatting consistent
In text referencing missing or incomplete In text referencing incomplete/incorrect In text referencing complete and consistent In text referencing complete and consistent
<2 2-3 3-4 4-5
Appendix R code (max 8) Annotations poor, lacking, or missing Annotations not as clear, may not explain both steps and outputs/results. Some errors in explanations Annotations clear, explaining both steps and outputs/results. Some errors in explanations or some detail missing Annotations clear and detailed, explaining both steps and outputs/results
<3 3-5 5-7 7-8
Scientific writing (max 5) No or minimal evidence of structure or organisation of information Paper written in a suitable manner with correct headings Paper written in a clear manner with ideas logically sequenced, some structural issues Paper written in a clear manner with ideas logically sequenced and appropriate headings
Poor paragraph structure and organisation Paragraph structure and organisation needs work Well-developed paragraphs and sentences. Topic sentences used Paragraph and sentence structure excellent. Topic sentences used
Scientific writing style needs work Poor scientific writing style (concise and non-repetitive) Concise writing and non-repetitive. Good scientific writing style Concise writing and non-repetitive. Excellent scientific writing style.
Inconsistent use of tenses and/or use of personal pronouns Non-personal writing style, may shift between tenses Correct tense and non-personal writing style Correct tense and non-personal writing style
More than 6 spelling and/or grammatical errors Less than 6 spelling and/or grammatical errors 2 - 4 spelling and/or grammatical errors No spelling errors, 1 or 2 grammatical errors
<2 2-3 3-4 4-5
