5.4 – Clinical trials

Introduction

Two areas of inquiry have contributed enormously to our understanding of experimental design: agriculture research (Verdooren 2020) and human subject clinical trials. This page outlines types of clinical trial designs and briefly introduces the subject of research ethics with human subject research (Huang and Hadian 2006). After completing this page, the reader will be able to identify key clinical trial types and recognize the fundamental ethical principles governing human subject research.

Although this page does not address the many concerns for ethical research in ecology and environmental studies, many parallels can be drawn (Crozier and Schulte-Hostedde 2014, Samuel and Richie 2022). To name just one example, both disciplines mandate a “do no harm,” principle. In clinical research this amounts to minimizing risks to human subjects; in environmental and ecology studies, acknowledging experimental studies through sampling or chemical releases can damage ecosystems (Resnik 2009).

An outline of clinical trials

Much has been written about biomedical research study design; a couple of accessible articles that can supplement the material presented here include Benson and Hart 2000, Concato et al 2000, Gabriella 2012. Even if you never work in clinical research, understanding how clinical trials are designed and under what circumstances limitations of particular designs arise is helpful to all of us who do experimental work. In Mike’s Workbook for Biostatistics we will present descriptions of research activities and step through situations where you will attempt to align the research by analogy to clinical trials. A second approach to learning about experimental design is to read about someone’s study, and reworking it in terms of a clinical trial design perspective. We will use a discussion of clinical trials, used in biomedical research to investigate effectiveness of treatments of disease, as our starting point for learning how statistics informs experimentation.

Types of clinical trials are distinguished by their design and include:

Case studies are a thorough analysis of an individual patient, or a few individuals (a group), focusing on symptoms, diagnostics, treatments and outcomes. Case studies can utilize quantitative or qualitative research methods. Case studies, of course, are not restricted to medical research. The main disadvantage of case studies is that by definition they are not generalizable to the population and, by their very nature, researcher subjectivity (choice of diagnostics, interpretations) and other kinds of bias may be challenging to control. A 2025 case study received considerable news coverage: a 60 year old man developed bromism — a toxic condition resulting from excessive exposure to bromides — consulting ChatGPT for health information (Eichenberger et al 2025).

Experimental studies are just that, research designs that apply techniques of experimental science — controls, randomization, attempts to account for sources of bias. They are intended to make direct comparisons among subjects assigned by the researcher to treatment groups. By definition experiments are longitudinal studies. Longitudinal studies are experimental or observational studies in which multiple observations are recorded for each individual and individuals are tracked over time. Many excellent resources about experimental design, beginning with R.A. Fisher’s 1935 book, The design of experiments, to Scheiner and Gurevitch (Editors) 2001, Design and Analysis of Ecological Experiments (2nd edition), to the many books on randomized control trials, eg, the 5th edition of Designing clinical research (2022), edited by Browner et al.

Observational (or epidemiological) studies — no direct intervention is administered and so observational studies tend to be retrospective; we identify individuals with and without the condition and attempt to identify associations between the condition and any number of potential causal factors.

Cross-sectional studies are examples of descriptive study designs. They take observations at one point in time on a variety of individuals. It can be used to associate factors with the condition in question, and can be used to estimate the prevalence of a condition in the population. Omair (2015) provides an accessible  summary of case and cross-sectional study designs.

Cross-sectional studies are referred to as method = cross.sectional in the package epiR.

Cohort studies involve a group of subjects (eg, patients) who receive the same treatment at the same time. A cohort consists of subjects who are linked in some way. It could be a trivial link, like the cohorting done at university (all incoming Freshmen students who enroll for a class offered at 9:30AM), or it could be based on shared experience due to an exposure event (eg, all passengers on a jet traveling with an index case or “patient zero”).

• Prospective cohort studies enroll people as cohorts at the beginning of a study and follow them over time
• Retrospective cohort studies may utilize archived records.

Cohort and other variations of observational studies (eg, case control, test-negative design), can establish associations between risk factors and conditions or specific adverse events, but cannot by themselves establish cause and effect (Benson and Hartz 2000). A search of PUBMED for “cohort study” reports more than 3.2 million studies since 1914. A 2025 prospective cohort study with more than 1800 pregnant women found no change in risk of major birth defects after maternal mRNA COVID-19 vaccination in the first trimester of pregnancy (Kayser et al 2025).

Cohort studies are referred to as method = cohort.count in the package epiR.

Case control studies are similar to cohort studies, except they are retrospective. Case refers to subjects with one or more characteristics of interest.  Used to infer the exposure risk factor by evaluating historical records. Omair (2016) provides an accessible  summary of cohort and case control study designs. Designed to identify associations between exposure and particular outcomes, case control studies are retrospective and observational studies: Retrospective because the outcomes are already known, and observational because the event was caused by nature, not experiment. In principle, researchers identify a number of cases with a particular outcome (eg, lung cancer), then attempt to match cases to individuals who do not have the outcome (controls). Work is done to look back to see if the exposure (eg, smoking) is more frequent in the case group than the control group.

Case control studies have several advantageous compared to other approaches: rapid to conduct compared to longitudinal studies (the even has already happened), and efficient because small sample sizes may be enough to reach conclusions. Among their limitations, however, is the problem of how to match cases with controls. Obvious matching accomplished by grouping by age categories, body mass index, gender, socio-economic status, and so on. Three papers authored by Wacholder published in American Journal of Epidemiology 1992 describe in detail, from theory to practice, case control selection (Wacholder 1992abc). A search of PUBMED for “case control” reports more than 400 thousand studies since 1950. A 2025 case control study with more than one million records found no effect of aluminum adjuvant in childhood vaccines on chronic autoimmunity, atopy or allergy, and neurodevelopmental disorders (Andersson et al 2025).

Case control studies are referred to as method = case.control in the package epiR.

Test-negative designs are a kind of case control studies where the cases are those testing positive for the specific agent and controls are those who test negative for the agent, but importantly, both groups are individuals seeking care for symptoms characteristic of exposure to the agent (Kissling et al 2009, Fukushima and Hirota 2017). This design is intended to reduce the effects of health-care seeking behavior, therefore minimizing possible selection bias, and, therefore false associations between exposure and disease.  Note however, that test-negative studies rely on test accuracy (discussed in Chapter 7.3 – Conditional Probability and Evidence Based Medicine); false negatives, where a person has the disease but test results return negative, incorrectly assigns the individual to the control group. This leads to underestimation of true effect of the intervention, eg, vaccination Fukushima and Hirota 2017).

Randomized Control (interventional or experimental) Trials (RCT): compares an experimental treatment group with a control) placebo group. The groups are assigned to groups randomly. Another variant of a RCT is a randomized clinical trial, with the only difference that the clinical trial compares different treatments and may not include a control group.

Double-blind: The “gold standard” RCT. Both the patient and those interacting with the patient do not know what treatment the patient has received.

Placebo: a treatment in name only. The placebo is a designed but medically ineffective agent given to a study subject. There is considerable debate about what a placebo should contain and as to the ethics and general merits of its use in clinical trials (Temple and Ellenberg 2000).

• active control or comparator studies are now common in place of placebo studies where treatment is clearly better than not giving the subject something of benefit (i.e., the placebo which is designed to not benefit the subject). Active control is not automatically a better choice than placebo control because they may be less effective in evaluating cause and effect (Temple and Ellenberg 2000).

The controlled clinical trial has a long history: Daniel’s vegetarian diet (Daniel’s training in Babylon, Book of Daniel, Old Testament, discussed in Bhatt 2010, h/t Treece 1990) — after ten days those on Daniel’s diet looked healthier than the others who ate the King’s prescribed meal of meat and wine; James Lind’s scurvy trials on board HMS Salisbury British ship in 1747 (references in Bhatt 2010). Randomized control trials (RCT) were introduced by Hill and others in 1946 study of streptomycin efficacy against tuberculosis. The effectiveness of RCT are now established and integral to regulation of drug development.

Use of clinical trials unfortunately has a longer history than recognition of human rights (Rice 2008). World War II Nazi medical research atrocities are well known (Berger 1990), so too the longitudinal study of Tuskegee syphilis study on African-Americans (Brandt 1978). But there are too many other examples: 1884 Hawaii, inoculation of Keanu by Dr. Arning with leprosy in exchange for commuting Keanu’s death sentence to life imprisonment (Keanu developed leprosy and died in 1890, Binford 1936); many studies of American Indians/Alaskan natives (Hodge 2012). Concerns about ethical treatment of participants in studies remains a challenge; see controversy over prospective randomized control trial (NCT00078182) of safety and efficacy of an HIV prophylactic tenofovir treatment in Cambodia (Singh and Mills 2005, Mills et al 2005).

Rules of conduct were established at Nuremberg, and subsequently extended and codified by Belmont Accords: core principles of respect for persons, beneficence, and justice. Ethical standards of who participates are institutionalized by IRB boards. True informed consent remains challenging (Rothwell et al 2021 and references therein).

Figure 1 shows how common in PUBMED randomized control trials and references to use of double blind design in research (Fig 1).

Figure 1. Search of PUBMED for “RCT” and “double blind” studies from 1950 to 2024.

Ethics of clinical and experimental research

Informed consent of subjects before proceeding in a clinical trial is a required, essential component of the design of a clinical trial. Guidelines for research conducted on human subjects originated from The Nuremberg Code (Shuster 1997). The Code was formulated 67 years ago, in August 1947, in Nuremberg, Germany, by American judges sitting in judgment of Nazi doctors accused of conducting torturous experiments on humans in the concentration camps (Shuster 1997). It served as a blueprint for today’s principles that ensure the rights of subjects in medical research. Achieving informed consent is not always straightforward (Nijhawan et al 2013), and we continue to see research that challenges ethical standards (eg, discussion in Suba 2014). Additionally, and perhaps less appreciated, the Nuremberg Code is justification for invasive animal research: animal research must precede human subject testing (Shuster 1997).

While informed consent is required, clearly, it is not enough. Emmanuel et al (2000) provide a framework for evaluating the ethics of a research program involving human subjects:

1. Value
2. Scientific validity
3. Fair subject selection
4. Favorable risk-benefit ratio
5. Independent review
6. Informed consent
7. Respect for enrolled subjects

An additional concept, clinical equipoise (Freedman 1987) is relevant. Freedman noted that the researcher must have “genuine uncertainty” with respect to the merits of each treatment, or an “honest null hypothesis.” If a consensus exists that one treatment is better than another, including placebo, then there is no null hypothesis and the research would be invalid (Emmanuel et al 2000). Take, for example, the suggestion that clinicians should withhold angiotensin converting inhibitors (ACE2) from their hypertensive Covid-19 patients (Fang et al 2020; discussed in Tignaneli et al 2020). The hypothesis comes from the observation that SARS-COV2, like SARS-Cov, binds with ACE2 receptor in order to invade the cell. Blocking ACE2 inhibitors then would reduce activation of pulmonary renin angiotensin system and subsequent lung injury. Tignaneli et al (2020) called this a case of clinical equipoise — they argued no evidence supports “routine discontinuation” of ACE inhibitors.

Returning to the framework, Emmanuel et al left the equipoise consideration off their list for a number of reasons — for example, clinical equipoise can lead researchers to view pursuit of research has the same weight as therapy. Importantly, the proposed framework shifts the focus from the researcher views to a structured process of oversight that may minimize exploitation. It emphasizes factors like independent review and informed consent as more reliable ethical safeguards.

Moreover, the Nuremberg Code is foundational but not the current, nor the sole guidance for research conducted on human subjects; modern biomedical research is guided by more comprehensive documents like the Declaration of Helsinki and the Belmont Report, in addition to federal regulations and Institutional Review Boards or IRB.

Guidelines mandate detail about experimental design.

Professional journals expect authors to provide detailed descriptions of all methodology, including aspects of experimental design. Fundamental to the aim of science, to increase our knowledge  An essential component of science To improve this kind of communication many journals and professional societies have promoted standards about what must be included in these descriptions. For example, efforts of the CONSORT, which stands for CONsolidated Standards Of Reporting Trials, at www.consort-statement.org, to improve the reporting of clinical trials by authors and provide guidelines for reviewers and editors is endorsed by more than 400 journals. The CONSORT checklist address

• Trial design
• Participants
• Interventions
• Outcomes
• Sample size
• Randomization
• Implementation
• Blinding
• Statistical methods

These nine elements were judged essential for authors to report how their study implemented or did not implement. The purpose of these items is to in order to improve reproducibility of published research. For animal research a similar list is available from ARRIVE (Kilkenny et al 2010). ARRIVE also addresses additional criteria and directs how these should be reported throughout the paper, not just in the methods section. Like CONSORT, hundred of journals have endorsed the ARRIVE 20-item checklist.

Clinical researchers must implement protocols to insure data management guidelines are followed. Clinical data management is a large topic in and of itself, so we won’t discuss this area further. However, good data management practice across disciplines share a number of features. For example, all data records should include metadata, where metadata refers to “information about data,” and would include enough information about the experiment, including

• dates and times of observations
• personnel
• facilities
• protocols used
• number of subjects
• list of variables with definitions
  • full name of variable plus any acronym
  • measurement units
  • instrumentation
  • notes about data quality
• conditions

and more (this is hardly an exhaustive list). Metadata therefore explains how data were obtained. Lists of variables are also called data dictionaries. If data are stored in spreadsheets, for example, then good practice includes including a worksheet with the metadata for the data set.

Note 1: We introduced metadata and data dictionaries with our discussion about data types in Chapter 3.1.

Questions

1. Be able to define the following terms

• case control
• case study
• cohort study
• cross sectional study
• observational study
• experimental study
• single arm trial
• single blind vs double-blinding in research design

2. Early in the Covid-19 pandemic, hydroxycholoquine was suggested for treating Covid-19 patients, and some called for prophylactic use of the malarial drug. Discuss the treatment hypothesis in the context of clinical equipoise.
3. Distinguish between case control prospective and case control retrospective studies and the kinds of inferences that can be made.

Quiz Chapter 5.4

Clinical trials

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Chapter 5 contents

• The basics explained
• Experiments
• Experimental and Sampling units
• Replication, Bias, and Nuisance variables
• Clinical trials
• Importance of randomization in experimental design
• Sampling from Populations
• References and suggested readings

5.1 – Experiments

How to put an experiment together

With the background behind us, we outline a designed experiment. Readers may wish to review concepts presented previously, in

• Chapter 2.4: Experimental design and rise of statistics in medical research.
• Chapter 3.4 – Estimating parameters
• Chapter 3.5 – Statistics of error

Experiments have the following elements

1. Define a reference population.
   1. All patients with similar angina symptoms — shortness of breath, chest pain, dizziness — but who have not suffered a heart attack, or myocardial infarction (Wu et al 2017);
   2. All adult frogs in a pond near industrial waste runoff (eg, Sowers et al 2009).
2. Define sample unit from the reference population (eg, as many patients as can be screened by the physician are recruited; all frogs visible to the biologist are captured).
3. Design sampling scheme from population (eg, random sampling vs. convenience/haphazard sampling).
4. Agree on the primary outcome or endpoint to be measured and whether additional secondary outcomes are to be observed and collected.
   1. For the patients with similar symptoms, we follow
   2. For the frogs, we count adults with developmental malformations.
5. Separate the sample into groups so that comparisons can be made
   1. We may be tempted to provide the same treatment and follow the group for improvement — that is the clinically valid approach; to be an experiment, we would use random assignment of individuals to one of two groups — half will receive a placebo, half will receive a beta blocker;,
   2. The frog example doesn’t follow this scheme — the event has already occured, it’s an observational study.
6. Devise a way to exclude or distinguish from the possible explanations, or alternative hypotheses.
   1. eg, for the physician, she will record how many of her patients fail to respond to treatment;
   2. for the biologist, noting presence or absence of parasites between the normal and abnormal frog defines the groups).

A basic experimental design looks like this.

1. One treatment, with two levels (eg, a control group and an experimental group)
2. A collection of individuals recruited from a defined population, esp. by random sampling.
3. Random assignment of multiple individuals to one of the two treatment levels.
4. The treatments are applied to each individual in the study.
5. A measure of response (the primary outcome) and additional features (secondary outcomes) are recorded for each individual.

Of course, there are other considerations, and by now, students reading this text should be able to identify what is missing from the list. (See quiz questions.)

Contrast with design of observational studies

Importantly, for observational studies, no matter how sophisticated the equipment used to measure the outcome variable(s), key steps outlined above are missing. Researchers conducting observational studies do not control allocation of subjects to treatment groups (steps 4 and 3 in the two above lists, respectively). Instead, they may use case-control approaches, where individuals with (case) and without (control) the condition (eg, lung cancer) are compared with respect to some candidate risk factor (eg, smoking). Both case and control groups likely have members who smoke, but if there is an association between smoking and lung cancer, then more smokers will be in the case group compared to the control group.

A cohort study, also a form of observational study, is similar to case-control except that the outcome status is not known. A cohort study includes, in our example, smokers and non smokers who share other characteristics: age, medical history, etc. The other kind of design you will see is the cross-sectional study. Cross-sectional studies are descriptive studies, and, therefore also observational. Primary outcomes along with additional characteristics and outcomes are measured for a representative subsample, or perhaps even an entire population. Cross-sectional studies are used to absolute and relative risk rates. In ecology and evolutionary biology cross sectional studies are common, eg, comparisons of species for metabolic rate (Darveau et al 2005) or life history traits (Jennings et al 1998), and specialized statistical approaches that incorporate phylogenetic information about the species are now the hallmark of these kinds of studies.

A word on outcomes of an experiment. Experiments should be designed to address an important question. The outcomes the researcher measures should be directly related to the important question. Thus, in the design of clinical trials, researchers distinguish between primary and secondary outcomes or endpoints. In educational research the primary question is whether or not students exposed to different teaching styles (eg, lecture-style or active-learning approaches) score higher on an knowledge-content exams. The primary outcome would be the scores on the exams; many possible secondary outcomes might be collected including student’s attitudes towards the subject or perceptions on how much they have learned.

About Primary and Secondary Outcomes.

Consider a evolutionary physiology project on species differences and effects of body size on burst running speed of terrestrial lizards (eg, Bauwens et al 1995; Dohm et al 1998; Labonte et al 2024). Our independent variables, the things we believe influence running performance — and therefore we wish to control for — species, body size, core body temperature, body condition, race quality (see cited papers for these and other examples).

What do we measure? What are the likely primary vs. secondary outcomes for a this kind of project?

Primary outcomes are the direct, definitive measures of maximal locomotor performance. Examples may include

• Maximum Burst Speed: The absolute highest velocity achieved by the lizard over a short distance or time interval (eg, across a 0.5-meter interval).
• Maximum Acceleration: The highest rate of speed change during the start of the burst or during a sudden sprint increase.
• Stride Length: The physical distance covered during a single full stride cycle at maximum speed.
• Stride Frequency: The number of strides taken per second at maximum speed.

And what other measures may be of interest — secondary outcomes — those that may help explain why or how the primary performance metrics change across the independent variables. To list a few,

• Stance and Swing Duration: The individual time durations spent with feet on the ground (stance) vs. in the air (swing).
• Gait Transition Thresholds: The speed at which a lizard shifts from a walk/trot to a high-speed gallop or bipedal running (if applicable to the species).
• Fatigue Rate: The decline in burst speed across successive trials or over a sustained treadmill run.
• Trial-to-Trial Repeatability: The statistical consistency (intraclass correlation coefficient) of a lizard’s performance across multiple bursts.

In a prospective cohort study, we may track participants over time to observe how baseline factors predict the development or worsening of type 2 diabetes (eg, Gao et al 2026). Progression over a two year followup would be measured by incident clinical events or the longitudinal rate of change in biomarkers. Possible primary outcomes are

• Glycated Hemoglobin (HbA1c): A biomarker reflecting average blood glucose levels over the past 2 to 3 months (glycemic control).
• Fasting Plasma Glucose (FPG): Glucose concentration in the blood after an overnight fast (acute baseline glycemic regulation).
• T2D Complication Stage: A clinical score or categorization indicating the presence and severity of microvascular or macrovascular damage.

Secondary outcomes may include

• Fasting Lipid Profile
• C-Reactive Protein
• Body Mass Index
• Estimated Glomerular Filtration Rate

If one or more primary outcomes are significant, this reveals whether an intervention or exposure (like a vegan diet or income level) changes the clinical course of Type 2 Diabetes. Secondary outcomes are critical for explaining how and why it happens. Secondary outcomes provide mechanistic context.

Some caution is warranted — imagine if, after conducting the study, we find diet, age, or income did not statistically affect your primary outcome, but did significantly alter a secondary outcome (eg, improved lipid profiles). What to report? We state in your Abstract and Discussion that the study failed to meet its primary objective, and that any significant secondary findings are exploratory. We cannot claim that a diet “successfully manages diabetes progression” based solely on a secondary marker (cf discussion and suggsted guidelines in Butcher et al 2022).

Example of an experiment.

We’ll work through a familiar example. The researcher is given the task to design a study to test the efficacy in reducing tension headache symptoms by a new pain reliever. There are many possible outcomes: blurry vision, duration, frequency, nausea, need for and response to pain medication, and level of severity (Mayo Clinic). The drug is packaged in a capsule and a placebo is designed that contains all ingredients except the new drug. Forty subjects with headaches are randomly selected from a population of headache sufferers. All forty subjects sign the consent form and agree to be part of the study. The subjects also are informed that while they are participating in the research study on a new pain reliever, each subject has a 50-50 chance of receiving a placebo and not the new drug. The researcher then randomly assigns twenty of the subjects to receive the drug treatment and twenty to receive the placebo, places either a placebo pill of the treatment pill into a numbered envelope and gives the envelopes to a research partner. The partner then gives the envelopes to the patients. Both patients and the research partner are kept ignorant of the assignment to treatment.

We can summarize this most basic experimental design in a table, Table 1.

Table 1. Simple formulation of a 2X2 experiment (aka 2×2 contingency table).

where N is the total number of subjects, a is number of subjects who DID receive the treatment AND got better, c is number of subjects who DID NOT receive the treatment, but DID get better, b is number of subjects who DID receive the treatment and DID NOT get better, and d is number of subjects who DID NOT receive the treatment and DID NOT get better.

And our basic expectation is that we are testing whether or not treatment levels were associated with subjects “getting better” as measured on some scale.

One possible result of the experiment, although unlikely, all of the negative outcomes are found in the group that did not receive the treatment (Table 2).

Table 2. One possible outcome of our 2X2 experiment.

Results of an experiment probably won’t be as clear as in Table 2. Treatments may be effective, but not everyone benefits. Thus, results like Table 3 may be more typical.

Table 3. A more likely outcome of our 2X2 experiment.

Must a control group always be included in an experiment?

To be an experiment, yes. However, it is not true that the control treatment must only be applied to separate, individual subjects. For example, a common experimental design would be repeated measures of an outcome, like behavior (eg, feeding response) before and after an intervention is applied. Thus, the individual is its own control or reference point. For example, Dohm et al (2008) measured feeding and locomotor behavior of adult toads (Rhinella marina, formerly Bufo marinus) prior to and after ozone exposure. We concluded that a single 4-h exposure to O₃ depressed toad feeding behavior, but had little effect on voluntary locomotor behavior. Typically, the design randomly assigns individuals to the  treatments — some individuals get the exposure first, then the control exposure second; others, again selected at random, receive the control exposure first, then the active exposure.

The within subjects design is an important method in biological research; these designs offer control for individual differences as confounding variables and require fewer participants compared to separate control group designs. This design is effective provided there is little risk of carryover effects; if the effects of a previous condition linger, then it may influence a participant’s response in subsequent conditions, confounding the results. For our study (Dohm et al 2008) we used a modified within-subjects design by employing a split plot design. We return to within-subjects designs in Chapter 14.4 – Randomized block design.

Questions

1. When I updated this page in 2020, we were in our fifth month of the coronavirus pandemic of 2019-2020. Daily it seemed, we heard news about “promising coronavirus treatments,” but no study had at the time been published that would meets our considerations for a proper experiment. However, on May 1, 2020, the FDA issued an emergency use authorization for use of the antiviral drug remdesivir (Gilead), based on early results of clinical trials reported 29 April in The Lancet (Wang et al 2020). Briefly, their study included 158 treated with does of the antiviral drug and 79 provided with a placebo control. Both groups were treated otherwise the same. Improvement over 28 days was recorded: 62 improved with Placebo and 133 improved receiving dose of remdesivir.
   1. Using the background described on this page, list the information needed to design a proper experiment. Using your list, review the work described in The Lancet article and check for evidence that the trial meets these requirements.
   2. Create a 2×2 table with the described results from The Lancet article.
2. Consider our hazel tea and copper solution experiment described in Chapter 3. The outcome variables (Tail length, tail percentage, Olive moment) are quantitative, not categorical. Create a table to display the experimental design.
3. For the migraine example, identify elements of the design that conform to the randomized control trial design described in Chapter 2.4.

Quiz Chapter 5.1

Experiments

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Chapter 5 contents

• The basics explained
• Experiments
• Experimental and Sampling units
• Replication, Bias, and Nuisance Variables
• Clinical trials
• Importance of randomization in experimental design
• Sampling from Populations
• References and suggested readings