Observational study in the context of Inspection

Reproducibility, closely related to replicability and repeatability, is a major principle underpinning the scientific method. For the findings of a study to be reproducible means that results obtained by an experiment or an observational study or in a statistical analysis of a data set should be achieved again with a high degree of reliability when the study is replicated. There are different kinds of replication but typically replication studies involve different researchers using the same methodology. Only after one or several such successful replications should a result be recognized as scientific knowledge.

Statistics (from German: Statistik, orig. "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments.

When census data (comprising every member of the target population) cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation.

In mathematics and statistics, the arithmetic mean ( /ˌærɪθˈmɛtɪk/ arr-ith-MET-ik), arithmetic average, or just the mean or average is the sum of a collection of numbers divided by the count of numbers in the collection. The collection is often a set of results from an experiment, an observational study, or a survey. The term "arithmetic mean" is preferred in some contexts in mathematics and statistics because it helps to distinguish it from other types of means, such as geometric and harmonic.

Arithmetic means are also frequently used in economics, anthropology, history, and almost every other academic field to some extent. For example, per capita income is the arithmetic average of the income of a nation's population.

Experimental data in science and engineering is data produced by a measurement, test method, experimental design or quasi-experimental design. In clinical research any data produced are the result of a clinical trial. Experimental data may be qualitative or quantitative, each being appropriate for different investigations.

Generally speaking, qualitative data are considered more descriptive and can be subjective in comparison to having a continuous measurement scale that produces numbers. Whereas quantitative data are gathered in a manner that is normally experimentally repeatable, qualitative information is usually more closely related to phenomenal meaning and is, therefore, subject to interpretation by individual observers.

A longitudinal study (or longitudinal survey, or panel study) is a research design that involves repeated observations of the same variables (e.g., people) over long periods of time (i.e., uses longitudinal data). It is often a type of observational study, although it can also be structured as longitudinal randomized experiment.

Longitudinal studies are often used in social-personality and clinical psychology, to study rapid fluctuations in behaviors, thoughts, and emotions from moment to moment or day to day; in developmental psychology, to study developmental trends across the life span; and in sociology, to study life events throughout lifetimes or generations; and in consumer research and political polling to study consumer trends. The reason for this is that, unlike cross-sectional studies, in which different individuals with the same characteristics are compared, longitudinal studies track the same people, and so the differences observed in those people are less likely to be the result of cultural differences across generations, that is, the cohort effect. Longitudinal studies thus make observing changes more accurate and are applied in various other fields. In medicine, the design is used to uncover predictors of certain diseases. In advertising, the design is used to identify the changes that advertising has produced in the attitudes and behaviors of those within the target audience who have seen the advertising campaign. Longitudinal studies allow social scientists to distinguish short from long-term phenomena, such as poverty. If the poverty rate is 10% at a point in time, this may mean that 10% of the population are always poor or that the whole population experiences poverty for 10% of the time.

In statistical hypothesis testing, a result has statistical significance when a result at least as "extreme" would be very infrequent if the null hypothesis were true. More precisely, a study's defined significance level, denoted by ${\displaystyle \alpha }$, is the probability of the study rejecting the null hypothesis, given that the null hypothesis is true; and the p-value of a result, ${\displaystyle p}$, is the probability of obtaining a result at least as extreme, given that the null hypothesis is true. The result is said to be statistically significant, by the standards of the study, when ${\displaystyle p\leq \alpha }$. The significance level for a study is chosen before data collection, and is typically set to 5% or much lower—depending on the field of study.

In any experiment or observation that involves drawing a sample from a population, there is always the possibility that an observed effect would have occurred due to sampling error alone. But if the p-value of an observed effect is less than (or equal to) the significance level, an investigator may conclude that the effect reflects the characteristics of the whole population, thereby rejecting the null hypothesis.

In metaphysics and statistics, a causal model (also called a structural causal model) is a conceptual model that represents the causal mechanisms of a system. Causal models often employ formal causal notation, such as structural equation modeling or causal directed acyclic graphs (DAGs), to describe relationships among variables and to guide inference.

By clarifying which variables should be included, excluded, or controlled for, causal models can improve the design of empirical studies and the interpretation of results. They can also enable researchers to answer some causal questions using observational data, reducing the need for interventional studies such as randomized controlled trials.

Clinical study design is the formulation of clinical trials and other experiments, as well as observational studies, in medical research involving human beings and involving clinical aspects, including epidemiology . It is the design of experiments as applied to these fields. The goal of a clinical study is to assess the safety, efficacy, and / or the mechanism of action of an investigational medicinal product (IMP) or procedure, or new drug or device that is in development, but potentially not yet approved by a health authority (e.g. Food and Drug Administration). It can also be to investigate a drug, device or procedure that has already been approved but is still in need of further investigation, typically with respect to long-term effects or cost-effectiveness.

Some of the considerations here are shared under the more general topic of design of experiments but there can be others, in particular related to patient confidentiality and medical ethics.

Naturalistic observation, sometimes referred to as fieldwork, is a valuable form of empirical data collection in research methodology across numerous fields of science (including ethology, anthropology, linguistics, social sciences, and psychology) in which data are collected as they occur in nature, without any manipulation by the observer. Examples range from watching an animal's eating patterns in the forest to observing the behavior of students in a school setting. During naturalistic observation, researchers take great care using unobtrusive methods to avoid interfering with the behavior they are observing. Naturalistic observation contrasts with analog observation in an artificial setting that is designed to be an analog of the natural situation, constrained so as to eliminate or control for effects of any variables other than those of interest. There is similarity to observational studies in which the independent variable of interest cannot be experimentally controlled for ethical or logistical reasons.

Naturalistic observation has both advantages and disadvantages as a research methodology. Observations are more credible because the behavior occurs in a real, typical scenario as opposed to an artificial one generated within a lab. Behavior that could never occur in controlled laboratory environment can lead to new insights. Naturalistic observation also allows for study of events that are deemed unethical to study experimentally, such as the impact of high school shootings on students attending the high school. However, because extraneous variables cannot be controlled as in a laboratory, it is difficult to replicate findings and demonstrate their reliability. In particular, if subjects know they are being observed they may behave differently than otherwise. It may be difficult to generalize findings of naturalistic studies beyond the observed situations.

Building science is the science and technology-driven collection of knowledge to provide better indoor environmental quality (IEQ), energy-efficient built environments, and occupant comfort and satisfaction. Building physics, architectural science, and applied physics are terms used for the knowledge domain that overlaps with building science. In building science, the methods used in natural and hard sciences are widely applied, which may include controlled and quasi-experiments, randomized control, physical measurements, remote sensing, and simulations. On the other hand, methods from social and soft sciences, such as case study, interviews & focus group, observational method, surveys, and experience sampling, are also widely used in building science to understand occupant satisfaction, comfort, and experiences by acquiring qualitative data. One of the recent trends in building science is a combination of the two different methods. For instance, it is widely known that occupants' thermal sensation and comfort may vary depending on their sex, age, emotion, experiences, etc. even in the same indoor environment. Despite the advancement in data extraction and collection technology in building science, objective measurements alone can hardly represent occupants' state of mind such as comfort and preference. Therefore, researchers are trying to measure both physical contexts and understand human responses to figure out complex interrelationships.

Building science traditionally includes the study of indoor thermal environment, indoor acoustic environment, indoor light environment, indoor air quality, durability, moisture damage, and building resource use, including energy and building material use. These areas are studied in terms of physical principles, relationship to building occupant health, comfort, and productivity, and how they can be controlled by the building envelope and electrical and mechanical systems. The National Institute of Building Sciences (NIBS) additionally includes the areas of building information modeling, building commissioning, fire protection engineering, seismic design and resilient design within its scope.

In genomics, a genome-wide association study (GWA study, or GWAS), is an observational study of a genome-wide set of genetic variants in different individuals to see if any variant is associated with a trait. GWA studies typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits like major human diseases, but can equally be applied to any other genetic variants and any other organisms.

When applied to human data, GWA studies compare the DNA of participants having varying phenotypes for a particular trait or disease. These participants may be people with a disease (cases) and similar people without the disease (controls), or they may be people with different phenotypes for a particular trait, for example blood pressure. This approach is known as phenotype-first, in which the participants are classified first by their clinical manifestation(s), as opposed to genotype-first. Each person gives a sample of DNA, from which millions of genetic variants are read using SNP arrays. If there is significant statistical evidence that one type of the variant (one allele) is more frequent in people with the disease, the variant is said to be associated with the disease. The associated SNPs are then considered to mark a region of the human genome that may influence the risk of disease.