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What is the Difference Between a Dissertation and a Thesis?

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And to make it even more confusing, some institutions or departments will even use the terms differently!

But what are we all really talking about when we refer to a dissertation or a thesis? And does the term you use actually impact on what you actually end up writing?

This article covers the main differences between a dissertation and thesis, and how the terms may differ depending on the course, university and location.

What is a dissertation?

A dissertation is a piece of academic writing centred around original research. In their dissertations, students review existing research but also build on this with unique hypotheses and approaches.

A dissertation can be used to disprove a previous theory or take existing theories and research in a new direction. It is a large research project that is usually completed at the end of the academic year.

Usually, a dissertation starts with a dissertation proposal , which is approved by a study supervisor. The student then completes the research and writes up the methodology , findings, evaluations and conclusions from the research.

Dissertations can be undertaken by both undergraduate and postgraduate students. At undergraduate level the word count is around 5,000 to 8,000 and at postgraduate level it is usually 10,000 to 15,000.

What is a thesis?

A thesis is an academic paper covering an in-depth review of existing research in a particular discipline. It will involve an academic argument, although it doesn’t usually require original research from the student. The existing research is used to support and evaluate the proposed argument.

A thesis is not usually required at undergraduate level and is more common at postgraduate level.

This large piece of written-up research is usually completed at the end of a masters degree. Some masters courses require a thesis to graduate.

Differences between a dissertation vs thesis

The main purpose of a writing a dissertation is to add new findings to the existing literature in that field with original research. Whereas theses tend to evaluate existing findings, as their purpose is to demonstrate knowledge and skills within the course’s subject matter.

In terms of how long it takes to complete a thesis or dissertation project, a thesis is typically shorter than a dissertation since there are fewer original research aspects involved. This means that it will probably take less time. However, this can differ depending on the university and the course.

Dissertations sometimes require an oral presentation, known as a viva , where findings are showcased to academics who ask questions about the research. Theses usually do not require this.

The root of the words 

The word ‘dissertation’ originates from the Latin word ‘dissertare’, meaning to continue to discuss and the Latin word ‘disserere’ which means to examine and discuss .

The word ‘thesis’ originally comes from the Greek word ‘tithenai’, which means to place or position. This later evolved into the Latin ‘thesis’, which had two meanings: an abstract question and to put something forward .

Similarities between a dissertation vs thesis

Although there are some key differences between a dissertation and a thesis, there are also similarities.

• Both are generally long pieces of academic writing, much longer than a typical essay.
• Both explore a topic in depth, whether you are conducting totally unique research or structuring an argument based on existing research.
• Both are considered a final project and usually required to graduate from a degree, masters or PhD. Students can graduate without a thesis or dissertation if they choose to complete a postgraduate diploma or postgraduate certificate instead.
• Excellent academic writing skills are highly important for both types of research project.

Is a dissertation harder than a thesis?

Though, the difficulty of a thesis or dissertation depends on your personal skill set. For instance, students that learn better by developing their own research ideas may find a dissertation easier than a thesis.

Difficulty can also depend on the level of the course. For instance, a thesis completed at doctorate level is likely to require more advanced knowledge than a thesis at undergraduate level.

The difficulty of either type of research project can also vary depending on the subject matter and the resources available to you.

Both dissertations and theses can be challenging, but don’t be put off by the thought of having to produce a larger body of work. Your supervisor will be there to support you.

Definitions depend on where you are

The terms ‘dissertation’ and ‘thesis’ are sometimes used interchangeably, and the meanings can differ depending on the country and university.

There are plenty of differences between the variant forms of English, such as British English and American English. Around the world, different English-speaking countries use the words ‘dissertation’ and ‘thesis’ differently. 

Generally, nations with British-based academic systems of university education use dissertation to refer to the body of work at the end of an undergraduate or masters level degree . British-based institutions generally use thesis to refer to the body of work produced at the end of a PhD . 

In countries and institutions that are based on the American system of education, the words tend to be used in reverse. However, institutions and even different departments in the same university can use the words differently. 

If you're in doubt, then stick with the way the university and department you're currently attending use the terms. 

Definitions can depend on the subject

In the UK, the terms ‘dissertation’ and ‘thesis’ are generally applied equally across institutions and subjects. 

However, in the US the meanings can differ between different subject areas. The term ‘thesis’ can be used to describe a piece of original research in US academia, whereas original research is usually referred to as a dissertation in the UK. 

If you’re studying in the US , you may complete a thesis at masters level in another subject area that involves wide-ranging reading and understanding rather than original research and still call it a thesis.

With so much interchangeability between the two terms, it’s understandable that there is often confusion in the debate between a dissertation vs thesis, as there is no clear answer. 

Always read specific course details to understand exactly what’s involved in the research project that you are required to produce.

Examples from US and UK universities

Georgetown University in the US refers to a dissertation and a thesis as both adding to your 'field of knowledge' . The University of Edinburgh recommends that you refer to your individual course handbook for guides to dissertations, so each department will have their own guidelines to using the word dissertation and thesis. At University College London they refer to a thesis as the piece of work at the end of an EngD, MPhil, MD(Res) or PhD, which are all research degrees. 

In conclusion

Ultimately, it doesn't really matter which word you use as both refer to a serious and lengthy piece of work where you can show what you have researched and understood as part of your postgraduate studies.

As long as you are referring to the piece of work that you are compiling in the same way as those in your department then you will avoid confusion.

It is important to check whether the research piece involves original research or expects you to build upon existing research.

Writing a dissertation or a thesis requires a substantial amount of planning and work and you don't want to let yourself down at the last hurdle with poor presentation of your work, so always keep an eye on your course or department guidelines.

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4 Little-Known Secrets about Great Dissertation Writing

Developing a thesis is one of the most complex undertakings by any student pursuing a higher degree. It requires a lot of research and consumes time. When drafting a dissertation, the student creates the information from nothing and this is the point where most students miss the opportunity to write a great dissertation. 

During their time in college, students can master the secrets that can help them write a compelling dissertation and earn good grades in their final results. 

Choose the best subject 

You will use your subject to create your thesis. The subject forms the foundation of your thesis and it should be a topic you are familiar with. If you choose a non-familiar topic, you will run into trouble when developing your arguments. 

One of the main things your lecturers will be looking for is your ability to cross-examine your questions and build a strong base of arguments. You must be able to redefine the challenges you want to address and choose the most applicable remedies to the questions/problems.

Research widely

One fundamental rule of dissertations is to create entirely  new ideas  even when the inspiration is not originally from your thoughts. The rule, therefore, calls for refining the ideas again and again until you arrive at the critical point where your thesis cannot be doubted.

Your dissertation will be your final paper and you must ensure you submit the best. If you are studying medicine, it will be good to start your research back 3,500 years ago when people in the east began to practice meditation as a way to trigger healing in the body. You can move on to the time of Greek philosophers who discovered the science of medicine, then examine how science has evolved to date and maybe its future. 

Professional dissertation writing help

The importance of professional online essay writing services to college students cannot be underestimated. The student gets to finish all his or her education assignments fast and within time. The professional essay writers will be able to do your assignment even if it’s the most complex. If you are  looking for dissertation services  within the UK, Uk.EduBirdie is one of the most established professional writing service providers. They professionally write your dissertation within the shortest time possible and at affordable prices.

Start with a draft

The amount of time you take to prepare all the information necessary for your dissertation is as important as the dissertation itself. Given this fact, you cannot rush and write your final draft in one sitting. You must go through successful steps to come to the final document. 

Drafting as a first step will help you merge all the information you have collected into one complete document. Write your draft as though you are writing the final document and include all the details like the objective, the main question, good plan, sufficient arguments, references, etc. 

Dissertation writing help importance

The dissertation is probably among the most significant work of literature you will be assigned in college, and it will potentially play a major role in your overall score. It is regarded as a means of demonstrating your research abilities.

Your knowledge in essay writing can be useful when writing your dissertation. It can indeed be challenging, but given its importance in university education, every college student should always be  ready to say write my dissertation for me  to professional dissertation writers like Writix. That’s a wise thing to do as it makes your life easy.

Refine your final paper 

You will be almost certain that your paper will be the most unique after you have spent ample time on all the first three points. Your final paper will not be difficult to write. Beginning with the title, include all that is required like your name, institution, field of study, and so on. 

Say, you are writing a  dissertation on sports . In that case, draw information from your draft and create a memorable abstract, acknowledgments, introduction, and move on with writing ensuring you do not omit any step and look knowledgeable in sports. Use all the writing tools available to achieve a unique paper. Make use of grammar tools, citation tools,  plagiarism checkers , formatting, and so on. 

Conclusion 

Your college dissertation can give you an idea of where to start your career life in employment or business if you take time to do thorough research. It diagnoses a current problem and provides relevant answers to it. You can use the answers to create much-needed solutions for society. You should therefore consider your dissertation as a most important achievement in your college education and give it all the important time it deserves. 

Author’s Bio:

Michael Turner works for a marketing agency as a senior advisor for their online and offline campaigns. He has been instrumental in boosting brand visibility and making it popular among the target audience. He’s a good academic writer as well and freelances for a thesis and dissertation writing service. His free time is for meditating, following business news and reviewing local food. 

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2024 Theses Master's

Cultural Heritage Preservation For At-Risk Communities - A Little Haiti, Miami Case Study

Wroy, Moisa

Thesis Statement: In the current contemporary urban landscape, preservation and development disproportionately place communities of color and migrant populations at-risk of losing their built and social fabric. This thesis will explore how this vulnerability arises, through examining the historic preservation toolkit relating to at-risk communities. This examination will be done through a case study of Little Haiti Miami. Little Haiti was chosen as the case study because the neighborhood is one of Miami's largest cultural neighborhoods that is formally recognized, by the Miami Commissioners Office, yet designated as a Historic District. This case study will provide a base to review Miami’s current cultural preservation policies and the ability to serve immigrant populations in retaining spatial and cultural autonomy. This thesis concludes with a set of policy policy critiques and recommendations. Rationale: The research in this thesis takes a people-based approach to what preservation means for cultural communities at risk of displacement through development. This will be guided under the premise that cultural displacement is not adequately looked at through different cultural communities, but rather through a one-size-fits-all approach. This thesis will look through a series of lenses to review development through the case of Little Haiti Miami as compared to other cultural communities and predominant Miami neighborhoods that have withstood displacement threats and have been able to use it to their economic advantage. In the field of Historic Preservation, there is a tendency to prioritize architectural style, often from the European perspective. This inclination can inadvertently neglect cultural neighborhoods, making it crucial to compare preserved structures to neglected ones. Recommendations: The thesis will summarize recommendations of new preservation policy regarding Management Overhaul Systems, which include the implementation of social media preservation and test calendars. Art and Mural expression as form of preservation of notable residents to integrate culture into preservation practice in Miami and the integration of Neighborhood Conservation Overlays into preservation with tools from the development toolkit that has previously acted as a form of community displacement.

Geographic Areas

• Florida--Miami--Little Haiti
• Historic preservation
• Historic districts
• Haitian Americans
• Mural painting and decoration, American
• Cultural property--Protection
• Economic development--Social aspects

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Department of english: dissertations, theses, and student research, breaking the rule of silence: childbirth and gendered power in efuru and the joys of motherhood.

Sunday Elliott Uguru , University of Nebraska-Lincoln Follow

First Advisor

Kwame Dawes

Date of this Version

A thesis submitted to the faculty of the Graduate College at the University of Nebraska-Lincoln in partial fulfillment of requirements for the degree of Master of Arts

Major: Engliah

Lincoln, Nebraska, May 2024

Copyright 2024, Sunday Elliott Uguru. Used by permission

This study examines the thematic preoccupation of childbirth in the formative period of feminist discourse in African literature through a critical study of selected novels of Igbo women of southeastern Nigeria. The novels studied represent the earliest published African texts in English by women. The period under focus falls within the emerging stage of Nigerian literary tradition in its written form with a dominant presence of men. This study investigates the women novelists' perspective toward the failure of male authored works to represent women's childbirth experience. Through a critical reading of Flora Nwapa's Efuru and Buchi Emecheta's The Joys of Motherhood , this study demonstrates how women novelists deploy the experience of childbirth in fiction to address the problematic perspective of male novelists in the literary representation of human experience. In investigating the female perspective to this situation, the study finds that the experience of childbirth in the novels is represented in ways that convey cultural meaning by turning a moment of childbirth into a site of expression for women. This represents a completely different move from the typical consideration of childbirth as a means of patriarchal control of women in Africa. Following abundant textual evidence that illustrates how women engage in the enactment of self-making against inherited image of weakness, the study concludes that the failure to represent childbirth in the narratives of men undermines the agency women derive from the experience of childbirth. The study therefore properly locates a reformulation of the Nigerian literary tradition to account for such crucial experience as childbirth to the intervention of women novelists. The study also shows how this failure is a form of gendered power that undermines the voice-laden potential of childbirth as women's site of expression beyond the literal voice.

Advisor: Kwame Dawes

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Decolonizing the Archaeological Photograph: Photography of the Commission des Monuments Historiques de l'Algérie, 1880-1910

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This thesis is a sort of “excavation” of the archaeological photograph. Although increasingly research is being done on classical archaeology’s colonial history and its implications for the discipline, much less has been written on the role photography plays in creating, upholding, and perpetuating colonial beliefs. Using photographs of Timgad produced by the Commission des Monuments Historiques de l’Algérie approximately between 1880-1910, this thesis examines the historical and visual factors that contribute to the meaning of an archaeological photograph and our belief in its relative objectivity, as well as the resulting political and epistemological consequences. It argues that these photographs are instruments of imperial dominance which function to lay claim to the monuments they depict and the history that they represent. This thesis traces the transformation of these monuments from archaeological material into historic sites both physically, through their restoration, and categorically, through their inclusion in the Commission des Monuments Historiques archive.

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Business Administration Dissertations

Effects of legitimacy, agency, and business risk on board structure in initial public offering firms: the moderating impact of ceo incentive alignment, ceo power, and stock market conditions.

Yousuf Hamed Al-Busaidi

Graduation Semester and Year

Document type.

Dissertation

Degree Name

Doctor of Philosophy in Business Administration

Business Administration

First Advisor

Abdul Rasheed

Board structure is generally seen as a means for acquiring resources, monitoring managerial behaviors, and enhancing firm legitimacy. Further, it is believed that the capacity of the board of directors to accomplish the above would improve with increasing outsider ratio, separation of CEO/chairman positions, and increasing board size. Previous studies found that an independent board plays an essential role in the long-term success of the firm. The role of the board is even more crucial during the firm's transformation from a privately held to a public company (i.e., Initial Pubic Offering). This study investigated the influence of IPO firms' risk on the structure of the board of directors by: (1) going beyond the agency paradigm and using Investor Confidence and Substitution Effects perspectives; (2) considering the many different kinds of risks (i.e., legitimacy, business, and agency risks) that are unique to IPO firms; and (3) exploring how CEO incentive alignment, CEO power, and stock market conditions moderate the relationship between risk and board structure. A sample of 410 domestic firms that made initial public offerings in the years 1997, 1998, 2001 and 2002 on the U.S. stock exchanges was collected. Results indicate significant negative relationship between the number of outside directors in the board of directors, on one hand, and firm age and the TMT equity ownership, on the other. The level of VC involvement was found to have significant negative relationship with separation of CEO/chairman positions. Finally, results suggest that board size is influenced positively by number of risk factors and negatively by blockholder equity and TMT equity. In addition, results suggest that the level and the direction of the influence of the independent variables are determined by the level of CEO stock options, the amount of CEO power at the time of IPO, and stock market conditions. More specifically, the increase in CEO stock option was found to reduce the impact of risk on the dependent variables. In addition, this study found that CEOs use their power and influence on the IPO firms to curb investors' pressure for increasing board independence. Finally, going public during hot markets was found to weaken the impact of risk on board independence.

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Business | Business Administration, Management, and Operations

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Al-Busaidi, Yousuf Hamed, "Effects Of Legitimacy, Agency, And Business Risk On Board Structure In Initial Public Offering Firms: The Moderating Impact Of CEO Incentive Alignment, CEO Power, And Stock Market Conditions" (2006). Business Administration Dissertations . 4. https://mavmatrix.uta.edu/businessadmin_dissertations/4

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Targeting Social Protection Programs with Machine Learning and Digital Data

• Aiken, Emily

Social protection programs are essential to assisting the poor, but governments andhumanitarian agencies are rarely resourced to provide aid to all those in need, so accuratetargeting of benefits is critical. In developed economies, targeting decisions typicallyrely on administrative income data or broad survey-based social registries. In low-income countries, however, poverty information is rarely reliable, comprehensive, orup-to-date. Novel sources of digital data — from mobile phones and satellites, inparticular — are well suited to fill this gap: they are predictive of wealth in low-incomecontexts and ubiquitously collected. The research studies in this dissertation designand evaluate new methods for targeting aid in low-resource contexts using machinelearning, satellite imagery, and mobile phone data, and evaluate these methods inlarge, real-world interventions. Across social protection programs in Togo, Afghanistan,and Bangladesh, the studies in this dissertation show that targeting methods based onmachine learning and digital data sources identify poor households more accuratelythan methods based on categorical eligibility criteria like geography or occupation, buttypically less accurately than traditional survey-based poverty measurement approaches.These results highlight the potential for digital data and machine learning to improve thetargeting of humanitarian aid, particularly when traditional poverty data are unavailableor out-of-date and in settings where conflict, environmental conditions, or healthconcerns render primary data collection infeasible. These studies also provide empiricalevidence on the limitations and risks of digital and algorithmic targeting approaches,including privacy, transparency, fairness, and digital exclusion.

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• v.60(9); 2016 Sep

Basic statistical tools in research and data analysis

Zulfiqar ali.

Department of Anaesthesiology, Division of Neuroanaesthesiology, Sheri Kashmir Institute of Medical Sciences, Soura, Srinagar, Jammu and Kashmir, India

S Bala Bhaskar

1 Department of Anaesthesiology and Critical Care, Vijayanagar Institute of Medical Sciences, Bellary, Karnataka, India

Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

INTRODUCTION

Statistics is a branch of science that deals with the collection, organisation, analysis of data and drawing of inferences from the samples to the whole population.[ 1 ] This requires a proper design of the study, an appropriate selection of the study sample and choice of a suitable statistical test. An adequate knowledge of statistics is necessary for proper designing of an epidemiological study or a clinical trial. Improper statistical methods may result in erroneous conclusions which may lead to unethical practice.[ 2 ]

Variable is a characteristic that varies from one individual member of population to another individual.[ 3 ] Variables such as height and weight are measured by some type of scale, convey quantitative information and are called as quantitative variables. Sex and eye colour give qualitative information and are called as qualitative variables[ 3 ] [ Figure 1 ].

Classification of variables

Quantitative variables

Quantitative or numerical data are subdivided into discrete and continuous measurements. Discrete numerical data are recorded as a whole number such as 0, 1, 2, 3,… (integer), whereas continuous data can assume any value. Observations that can be counted constitute the discrete data and observations that can be measured constitute the continuous data. Examples of discrete data are number of episodes of respiratory arrests or the number of re-intubations in an intensive care unit. Similarly, examples of continuous data are the serial serum glucose levels, partial pressure of oxygen in arterial blood and the oesophageal temperature.

A hierarchical scale of increasing precision can be used for observing and recording the data which is based on categorical, ordinal, interval and ratio scales [ Figure 1 ].

Categorical or nominal variables are unordered. The data are merely classified into categories and cannot be arranged in any particular order. If only two categories exist (as in gender male and female), it is called as a dichotomous (or binary) data. The various causes of re-intubation in an intensive care unit due to upper airway obstruction, impaired clearance of secretions, hypoxemia, hypercapnia, pulmonary oedema and neurological impairment are examples of categorical variables.

Ordinal variables have a clear ordering between the variables. However, the ordered data may not have equal intervals. Examples are the American Society of Anesthesiologists status or Richmond agitation-sedation scale.

Interval variables are similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced. A good example of an interval scale is the Fahrenheit degree scale used to measure temperature. With the Fahrenheit scale, the difference between 70° and 75° is equal to the difference between 80° and 85°: The units of measurement are equal throughout the full range of the scale.

Ratio scales are similar to interval scales, in that equal differences between scale values have equal quantitative meaning. However, ratio scales also have a true zero point, which gives them an additional property. For example, the system of centimetres is an example of a ratio scale. There is a true zero point and the value of 0 cm means a complete absence of length. The thyromental distance of 6 cm in an adult may be twice that of a child in whom it may be 3 cm.

STATISTICS: DESCRIPTIVE AND INFERENTIAL STATISTICS

Descriptive statistics[ 4 ] try to describe the relationship between variables in a sample or population. Descriptive statistics provide a summary of data in the form of mean, median and mode. Inferential statistics[ 4 ] use a random sample of data taken from a population to describe and make inferences about the whole population. It is valuable when it is not possible to examine each member of an entire population. The examples if descriptive and inferential statistics are illustrated in Table 1 .

Example of descriptive and inferential statistics

Descriptive statistics

The extent to which the observations cluster around a central location is described by the central tendency and the spread towards the extremes is described by the degree of dispersion.

Measures of central tendency

The measures of central tendency are mean, median and mode.[ 6 ] Mean (or the arithmetic average) is the sum of all the scores divided by the number of scores. Mean may be influenced profoundly by the extreme variables. For example, the average stay of organophosphorus poisoning patients in ICU may be influenced by a single patient who stays in ICU for around 5 months because of septicaemia. The extreme values are called outliers. The formula for the mean is

where x = each observation and n = number of observations. Median[ 6 ] is defined as the middle of a distribution in a ranked data (with half of the variables in the sample above and half below the median value) while mode is the most frequently occurring variable in a distribution. Range defines the spread, or variability, of a sample.[ 7 ] It is described by the minimum and maximum values of the variables. If we rank the data and after ranking, group the observations into percentiles, we can get better information of the pattern of spread of the variables. In percentiles, we rank the observations into 100 equal parts. We can then describe 25%, 50%, 75% or any other percentile amount. The median is the 50 th percentile. The interquartile range will be the observations in the middle 50% of the observations about the median (25 th -75 th percentile). Variance[ 7 ] is a measure of how spread out is the distribution. It gives an indication of how close an individual observation clusters about the mean value. The variance of a population is defined by the following formula:

where σ 2 is the population variance, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The variance of a sample is defined by slightly different formula:

where s 2 is the sample variance, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. The formula for the variance of a population has the value ‘ n ’ as the denominator. The expression ‘ n −1’ is known as the degrees of freedom and is one less than the number of parameters. Each observation is free to vary, except the last one which must be a defined value. The variance is measured in squared units. To make the interpretation of the data simple and to retain the basic unit of observation, the square root of variance is used. The square root of the variance is the standard deviation (SD).[ 8 ] The SD of a population is defined by the following formula:

where σ is the population SD, X is the population mean, X i is the i th element from the population and N is the number of elements in the population. The SD of a sample is defined by slightly different formula:

where s is the sample SD, x is the sample mean, x i is the i th element from the sample and n is the number of elements in the sample. An example for calculation of variation and SD is illustrated in Table 2 .

Example of mean, variance, standard deviation

Normal distribution or Gaussian distribution

Most of the biological variables usually cluster around a central value, with symmetrical positive and negative deviations about this point.[ 1 ] The standard normal distribution curve is a symmetrical bell-shaped. In a normal distribution curve, about 68% of the scores are within 1 SD of the mean. Around 95% of the scores are within 2 SDs of the mean and 99% within 3 SDs of the mean [ Figure 2 ].

Normal distribution curve

Skewed distribution

It is a distribution with an asymmetry of the variables about its mean. In a negatively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the right of Figure 1 . In a positively skewed distribution [ Figure 3 ], the mass of the distribution is concentrated on the left of the figure leading to a longer right tail.

Curves showing negatively skewed and positively skewed distribution

Inferential statistics

In inferential statistics, data are analysed from a sample to make inferences in the larger collection of the population. The purpose is to answer or test the hypotheses. A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. Hypothesis tests are thus procedures for making rational decisions about the reality of observed effects.

Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

In inferential statistics, the term ‘null hypothesis’ ( H 0 ‘ H-naught ,’ ‘ H-null ’) denotes that there is no relationship (difference) between the population variables in question.[ 9 ]

Alternative hypothesis ( H 1 and H a ) denotes that a statement between the variables is expected to be true.[ 9 ]

The P value (or the calculated probability) is the probability of the event occurring by chance if the null hypothesis is true. The P value is a numerical between 0 and 1 and is interpreted by researchers in deciding whether to reject or retain the null hypothesis [ Table 3 ].

P values with interpretation

If P value is less than the arbitrarily chosen value (known as α or the significance level), the null hypothesis (H0) is rejected [ Table 4 ]. However, if null hypotheses (H0) is incorrectly rejected, this is known as a Type I error.[ 11 ] Further details regarding alpha error, beta error and sample size calculation and factors influencing them are dealt with in another section of this issue by Das S et al .[ 12 ]

Illustration for null hypothesis

PARAMETRIC AND NON-PARAMETRIC TESTS

Numerical data (quantitative variables) that are normally distributed are analysed with parametric tests.[ 13 ]

Two most basic prerequisites for parametric statistical analysis are:

• The assumption of normality which specifies that the means of the sample group are normally distributed
• The assumption of equal variance which specifies that the variances of the samples and of their corresponding population are equal.

However, if the distribution of the sample is skewed towards one side or the distribution is unknown due to the small sample size, non-parametric[ 14 ] statistical techniques are used. Non-parametric tests are used to analyse ordinal and categorical data.

Parametric tests

The parametric tests assume that the data are on a quantitative (numerical) scale, with a normal distribution of the underlying population. The samples have the same variance (homogeneity of variances). The samples are randomly drawn from the population, and the observations within a group are independent of each other. The commonly used parametric tests are the Student's t -test, analysis of variance (ANOVA) and repeated measures ANOVA.

Student's t -test

Student's t -test is used to test the null hypothesis that there is no difference between the means of the two groups. It is used in three circumstances:

where X = sample mean, u = population mean and SE = standard error of mean

where X 1 − X 2 is the difference between the means of the two groups and SE denotes the standard error of the difference.

• To test if the population means estimated by two dependent samples differ significantly (the paired t -test). A usual setting for paired t -test is when measurements are made on the same subjects before and after a treatment.

The formula for paired t -test is:

where d is the mean difference and SE denotes the standard error of this difference.

The group variances can be compared using the F -test. The F -test is the ratio of variances (var l/var 2). If F differs significantly from 1.0, then it is concluded that the group variances differ significantly.

Analysis of variance

The Student's t -test cannot be used for comparison of three or more groups. The purpose of ANOVA is to test if there is any significant difference between the means of two or more groups.

In ANOVA, we study two variances – (a) between-group variability and (b) within-group variability. The within-group variability (error variance) is the variation that cannot be accounted for in the study design. It is based on random differences present in our samples.

However, the between-group (or effect variance) is the result of our treatment. These two estimates of variances are compared using the F-test.

A simplified formula for the F statistic is:

where MS b is the mean squares between the groups and MS w is the mean squares within groups.

Repeated measures analysis of variance

As with ANOVA, repeated measures ANOVA analyses the equality of means of three or more groups. However, a repeated measure ANOVA is used when all variables of a sample are measured under different conditions or at different points in time.

As the variables are measured from a sample at different points of time, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures: The data violate the ANOVA assumption of independence. Hence, in the measurement of repeated dependent variables, repeated measures ANOVA should be used.

Non-parametric tests

When the assumptions of normality are not met, and the sample means are not normally, distributed parametric tests can lead to erroneous results. Non-parametric tests (distribution-free test) are used in such situation as they do not require the normality assumption.[ 15 ] Non-parametric tests may fail to detect a significant difference when compared with a parametric test. That is, they usually have less power.

As is done for the parametric tests, the test statistic is compared with known values for the sampling distribution of that statistic and the null hypothesis is accepted or rejected. The types of non-parametric analysis techniques and the corresponding parametric analysis techniques are delineated in Table 5 .

Analogue of parametric and non-parametric tests

Median test for one sample: The sign test and Wilcoxon's signed rank test

The sign test and Wilcoxon's signed rank test are used for median tests of one sample. These tests examine whether one instance of sample data is greater or smaller than the median reference value.

This test examines the hypothesis about the median θ0 of a population. It tests the null hypothesis H0 = θ0. When the observed value (Xi) is greater than the reference value (θ0), it is marked as+. If the observed value is smaller than the reference value, it is marked as − sign. If the observed value is equal to the reference value (θ0), it is eliminated from the sample.

If the null hypothesis is true, there will be an equal number of + signs and − signs.

The sign test ignores the actual values of the data and only uses + or − signs. Therefore, it is useful when it is difficult to measure the values.

Wilcoxon's signed rank test

There is a major limitation of sign test as we lose the quantitative information of the given data and merely use the + or – signs. Wilcoxon's signed rank test not only examines the observed values in comparison with θ0 but also takes into consideration the relative sizes, adding more statistical power to the test. As in the sign test, if there is an observed value that is equal to the reference value θ0, this observed value is eliminated from the sample.

Wilcoxon's rank sum test ranks all data points in order, calculates the rank sum of each sample and compares the difference in the rank sums.

Mann-Whitney test

It is used to test the null hypothesis that two samples have the same median or, alternatively, whether observations in one sample tend to be larger than observations in the other.

Mann–Whitney test compares all data (xi) belonging to the X group and all data (yi) belonging to the Y group and calculates the probability of xi being greater than yi: P (xi > yi). The null hypothesis states that P (xi > yi) = P (xi < yi) =1/2 while the alternative hypothesis states that P (xi > yi) ≠1/2.

Kolmogorov-Smirnov test

The two-sample Kolmogorov-Smirnov (KS) test was designed as a generic method to test whether two random samples are drawn from the same distribution. The null hypothesis of the KS test is that both distributions are identical. The statistic of the KS test is a distance between the two empirical distributions, computed as the maximum absolute difference between their cumulative curves.

Kruskal-Wallis test

The Kruskal–Wallis test is a non-parametric test to analyse the variance.[ 14 ] It analyses if there is any difference in the median values of three or more independent samples. The data values are ranked in an increasing order, and the rank sums calculated followed by calculation of the test statistic.

Jonckheere test

In contrast to Kruskal–Wallis test, in Jonckheere test, there is an a priori ordering that gives it a more statistical power than the Kruskal–Wallis test.[ 14 ]

Friedman test

The Friedman test is a non-parametric test for testing the difference between several related samples. The Friedman test is an alternative for repeated measures ANOVAs which is used when the same parameter has been measured under different conditions on the same subjects.[ 13 ]

Tests to analyse the categorical data

Chi-square test, Fischer's exact test and McNemar's test are used to analyse the categorical or nominal variables. The Chi-square test compares the frequencies and tests whether the observed data differ significantly from that of the expected data if there were no differences between groups (i.e., the null hypothesis). It is calculated by the sum of the squared difference between observed ( O ) and the expected ( E ) data (or the deviation, d ) divided by the expected data by the following formula:

A Yates correction factor is used when the sample size is small. Fischer's exact test is used to determine if there are non-random associations between two categorical variables. It does not assume random sampling, and instead of referring a calculated statistic to a sampling distribution, it calculates an exact probability. McNemar's test is used for paired nominal data. It is applied to 2 × 2 table with paired-dependent samples. It is used to determine whether the row and column frequencies are equal (that is, whether there is ‘marginal homogeneity’). The null hypothesis is that the paired proportions are equal. The Mantel-Haenszel Chi-square test is a multivariate test as it analyses multiple grouping variables. It stratifies according to the nominated confounding variables and identifies any that affects the primary outcome variable. If the outcome variable is dichotomous, then logistic regression is used.

SOFTWARES AVAILABLE FOR STATISTICS, SAMPLE SIZE CALCULATION AND POWER ANALYSIS

Numerous statistical software systems are available currently. The commonly used software systems are Statistical Package for the Social Sciences (SPSS – manufactured by IBM corporation), Statistical Analysis System ((SAS – developed by SAS Institute North Carolina, United States of America), R (designed by Ross Ihaka and Robert Gentleman from R core team), Minitab (developed by Minitab Inc), Stata (developed by StataCorp) and the MS Excel (developed by Microsoft).

There are a number of web resources which are related to statistical power analyses. A few are:

• StatPages.net – provides links to a number of online power calculators
• G-Power – provides a downloadable power analysis program that runs under DOS
• Power analysis for ANOVA designs an interactive site that calculates power or sample size needed to attain a given power for one effect in a factorial ANOVA design
• SPSS makes a program called SamplePower. It gives an output of a complete report on the computer screen which can be cut and paste into another document.

It is important that a researcher knows the concepts of the basic statistical methods used for conduct of a research study. This will help to conduct an appropriately well-designed study leading to valid and reliable results. Inappropriate use of statistical techniques may lead to faulty conclusions, inducing errors and undermining the significance of the article. Bad statistics may lead to bad research, and bad research may lead to unethical practice. Hence, an adequate knowledge of statistics and the appropriate use of statistical tests are important. An appropriate knowledge about the basic statistical methods will go a long way in improving the research designs and producing quality medical research which can be utilised for formulating the evidence-based guidelines.

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Risky Business: Exploring What the EU Deforestation Regulation & Evolving Disclosure Requirements Mean for Your Company

Betsy Hickman

Nature Lead

Hope Bristow

Associate Director

Aviela Hochberg

Senior Consultant

• The Regulatory Landscape
• Aligning Policy with Best Practice
• Your Forest Positive Partner

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The recently adopted European Union’s Deforestation Regulation (EUDR) prohibits relevant commodities and products from being placed on the EU market unless they are deforestation-free. As of 30 December 2024, companies will be required to exercise detailed due diligence and verification to prove compliance for products and related commodities.  

Healthy forests are integral sources of both ecosystem services –such as carbon sequestration—and livelihoods for countless communities across the world. However, unsustainable agricultural practices, illegal logging, and rapid development are  driving  deforestation. The consequences of which are not just felt locally but have global ramifications.

Deforestation is one of the largest sources of  global annual emissions  and leading contributors to  biodiversity loss . The risk of human rights violations and land use conflict are often  linked  with deforestation. Whether you’re a clothing brand sourcing cellulosic fibers or a coffee roaster filling morning cups, addressing deforestation within your supply chain is one of the most significant measures you can take to reduce your emissions footprint and promote ethical supply chains.

Evolving Regulatory Landscape Puts Value on Forests

Growing legislation such as the   European Union Deforestation Free Products Regulation (EUDR)  has sent a resounding signal to companies that the global economy is inextricably linked and dependent upon nature and that the EU is serious about tackling the risk deforestation poses to the twin crises of climate change and nature loss. The cost of inaction from not addressing deforestation is evident in this legislation, too. The EU requires member states to establish penalties, including fines up to 4% of annual turnover to account for the damage done from deforestation-linked commodities entering the EU. This equates to a €2 million fine for a company with an annual turnover of €50 million – a significant financial loss that would raise concerns from investors and chief risk officers alike.

The EU requires member states to establish penalties, including fines up to 4% of annual turnover to account for the damage done from deforestation-linked commodities entering the EU.

There have also been major shifts within the landscape of voluntary requirements as well. Setting a zero-deforestation commitment is now a prerequisite for Science Based Targets for climate for companies with forest, land use, and agriculture (FLAG) emissions. The Science Based Targets Network similarly requires companies to include a No Conversion of Natural Ecosystems target within their requirements for SBTs for Nature. This push will require companies to examine their supply chains and sourcing practices, expand their responsible sourcing commitments to tackle all key deforestation-linked commodities, and continuously monitor and engage with their supply chain to ensure conformance.

Ensuring Your Deforestation Policy Aligns with Best Practice

Supply chains are complex, and tackling deforestation can be equally complicated. Anthesis Group’s  Forest Positive  team offers advice and technical support to our clients on their journeys to create deforestation- and conversion-free supply chains. In doing so, we utilize The Accountability Framework, a consensus-based guide for achieving ethical supply chains in agriculture and forestry. It offers clear guidance on implementation, monitoring, verification, and reporting on company progress toward addressing key environmental and social risks. Its accessibility and robust guidance are now integrated into leading initiatives such as the Science Based Target initiative (SBTi) and CDP.

The Accountability Framework was launched in 2019 by  a global coalition of environmental and human rights organizations , otherwise referred to as AFi Coalition. The Framework is based on international norms, good practices, and broad consensus. It represents the consensus of the AFi Coalition, as well as the expectations from leading companies, financial institutions, and international norms.

Your Partner in Accelerating a Forest Positive Journey

Anthesis is pleased to announce that we are now recognized by the AFi as a Delivery Partner, signaling our close collaboration with leading experts in the conservation community. Our Forest Positive experts utilize  AFi’s Core Principles   as a guide to support companies across a variety of sectors in assessing, prioritizing, and addressing deforestation-related risk, through services such as:

• Benchmarking : Benchmarking responsible sourcing policies and practices, targets, commitments, and procedures against the Accountability Framework to identify gaps and improvement opportunities.
• Target Setting & Road-Mapping : Setting strategic, context/science-based goals and targets that will drive corporate action to meet no-deforestation goals. Developing accompanying roadmaps with clear milestones to guide activities and assess progress towards targets.
• Policy Development & Implementation : Establishing/revising responsible sourcing policies. Creating training resources and developing supplier data collection systems to action responsible sourcing policies and evaluate sourcing activities. Embedding deforestation commitments into company’s internal business activities and investment strategies.
• Supplier Engagement : Engaging with suppliers through training, data collection, and policy alignment activities.
• Monitoring & Verification : Developing and/or utilizing monitoring and verification systems to detect deforestation risk and analyze resulting data.
• Communication & Progress Reporting : Providing internal and external communication support to highlight progress and performance. Transparently communicating an organization’s deforestation-free strategy, key metrics, initiatives, and progress towards targets.
• Human Rights/Social Impact : Conducting human rights impact assessments, investigation, and due diligence, assisting clients with developing ESG and human rights strategy and tools.
• Promoting Collaboration : Identifying opportunities to engage in multistakeholder partnerships that promote change at all levels-from local communities to restoring landscapes to transforming sectors.

We are the world’s leading purpose driven, digitally enabled, science-based activator. And always welcome inquiries and partnerships to drive positive change together.

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