Vocabulary for Junior Scientists

  1. Abduction: A scientific method of building theories by brainstorming explanations for known facts.
  2. Abstract: A concise summary of a research paper.
  3. Accuracy: How close a measurement is to the true value.
  4. Alternative Hypothesis: The statement that there is a difference or relationship between the variables being studied.
  5. Analysis: The process of interpreting and evaluating data.
  6. Applied research: Research focused on solving specific real-world problems.
  7. Autocorrelation: How a variable relates to itself over time (in a longitudinal study).
  8. Autoethnography: Studying your own experiences and perspectives within a cultural context.
  9. Availability heuristic: Judging something’s frequency based on easily recalled examples, not all evidence.
  10. Basic research: Research to expand general knowledge, not necessarily for practical use.
  11. Bias: An unintended influence on data or conclusions.
  12. Bias blind spot: The belief you’re less biased in your thinking compared to others.
  13. Bibliography: A list of all cited sources in a research paper.
  14. Bivariate correlation: A connection between just two variables.
  15. Case study: A close look at a single person, program, or event over time.
  16. Categorical variable: A variable with levels like male or female.
  17. Causation: When one variable causes a change in another variable.
  18. Citation: Reference to another published work in a research paper.
  19. Co-Investigator (Co-I): Plays a significant role in a research project alongside the PI, usually with expertise in a specific area contributing to the project.
  20. Comparison group: A group in an experiment that is different from the main group.
  21. Condition: A different version of something being tested in an experiment.
  22. Confidence Interval: A range of values that is likely to contain the true population parameter with a certain level of confidence (usually 95%).
  23. Confirmation bias: Tendency to favor evidence supporting a belief.
  24. Conclusion: A statement that summarizes the findings of an experiment.
  25. Confederate: An actor playing a specific role for the experimenter.
  26. Confound: An alternative explanation for a research finding.
  27. Construct Validity: How well a concept variable is operationalized.
  28. Constant: A factor in an experiment that is kept the same.
  29. Constructivism: Belief that knowledge is created, not discovered.
  30. Content analysis: Examining communication to find patterns or biases.
  31. Content validity: How well a measure captures a complete concept.
  32. Control group: A group in an experiment that is not exposed to the variable being tested.
  33. Convenience sampling: Using readily available participants.
  34. Convergent design: Combining quantitative and qualitative data collection.
  35. Convergent validity: How well a measure matches similar measures.
  36. Correlation: A statistical relationship between two variables.
  37. Correlation coefficient: A number showing the strength and direction of a relationship between two variables.
  38. Covariance: The extent in which two variables are related.
  39. Criterion validity: How well a measure predicts a relevant outcome.
  40. Critical thinking: Evaluating information and reasoning for accuracy and value.
  41. Cronbach’s alpha: A statistic measuring a scale’s internal consistency.
  42. Cross-sectional correlation: A correlation between variables measured at the same time in a longitudinal study.
  43. Data: Information collected through observation or measurement.
  44. Data Analyst: Analyzes and interprets research data using statistical methods and software.
  45. Deductive logic: Reasoning from general principles to specific conclusions.
  46. Dependent variable: The variable that is measured in an experiment (as opposed to manipulated).
  47. Descriptive Statistics: Methods used to summarize, describe, and organize data.
  48. Design confound: A threat to an experiment’s validity when another variable influences the results.
  49. Directionality problem: The unclear cause-and-effect relationship in correlational studies.
  50. Discriminant sampling: Selecting data sources to support an emerging theory in grounded theory studies.
  51. Discriminant validity: How well a measure avoids correlating with unrelated concepts.
  52. Effect Size: A measure of the magnitude of the effect of one variable on another.
  53. Empiricism: Relying on verifiable evidence from observations and experiments to form conclusions.
  54. Ethics: Moral principles that guide scientific research.
  55. Ethnography: A research method studying a culture in its natural environment.
  56. Experimental design: The way an experiment is set up and conducted.
  57. External validity: How well results apply to individuals outside the study.
  58. Evidence-based treatment: A therapy approach proven effective through research.
  59. Explanatory sequential design: A research method combining quantitative and qualitative data to explain findings.
  60. Exploratory sequential design: A research method using qualitative data to guide later quantitative data collection.
  61. External validity: How well study results represent a broader population or setting.
  62. Extreme case sampling: A sampling method focusing on unusual or atypical cases, often used in qualitative research.
  63. Face validity: Whether a measure seems like an accurate way to represent a concept.
  64. Falsifiability: The ability to disprove a scientific theory through evidence.
  65. Frequency Distribution: A table or graph that shows how often each value of a variable occurs.
  66. Gatekeeper: Someone who helps a researcher access a specific cultural group.
  67. Generalizability: How well a study’s subjects and setting represent a larger population or context.
  68. Grounded theory study: A research method where a theory is built based on data collected from a specific issue.
  69. h-index: highest number h such that h of their articles have at least h citations each.  Way of figuring research productivity
  70. High tier journal: A journal with a high impact factor and rigorous peer review.
  71. Highly cited: An article that has been cited at least 10 times.
  72. Histogram: A type of frequency distribution that uses bars to represent the number of data points that fall within a specific range of values.
  73. Hypothesis: A proposed explanation that can be tested through experimentation.
  74. Hypothesis testing: Using data to determine if a hypothesis is supported.
  75. i10-index: number of author publications that have been cited at least 10 times. Focuses on highly cited articles.
  76. Impact factor: A measure of the importance of a journal based on citations.
  77. Independent variable: The variable that is manipulated in an experiment to see its effect on the dependent variable.
  78. Inductive reasoning: Using specific observations to form a general rule.
  79. Inferential Statistics: Methods used to draw conclusions about the population based on the sample data.
  80. Informed Consent: Permission to participate in research after understanding its risks and benefits.
  81. Internal Reliability: A measure that gives consistent results, even if worded differently.
  82. Internal Validity: Whether cause-and-effect supports a particular piece.
  83. Institutional Animal Care and Use Committee (IACUC): University research ethics board for animal subjects research.
  84. Institutional Review Board (IRB): University research ethics board for human subjects research.
  85. Interrater reliability: Agreement between observers when rating something.
  86. Interval scale: A type of measurement where intervals between numbers are equal.
  87. Key informant: A person in a study who provides valuable insights and can connect you with others who can help.
  88. Known-groups paradigm: A way to test if a measure works by comparing groups already known to differ on the thing being measured.
  89. Lab Technician: Performs technical tasks essential for research projects, such as preparing samples, maintaining equipment, and conducting routine assays.
  90. Literature review: An overview of existing research on a topic.
  91. Manipulated variable: The variable controlled by the researcher in an experiment (e.g., dosage of a drug).
  92. Manuscript: A written draft of a research paper.
  93. Margin of error of the estimate: Difference between the population mean and the sample mean, often listed as plus/minus a percentage.
  94. Matched groups: A design where participants similar on a certain measure are grouped, then randomly assigned to different conditions in an experiment.
  95. Mean: The average of a set of numbers, calculated by adding all the values and dividing by the number of values.
  96. Measured variable: The variable in a study that is observed and recorded by the researcher.
  97. Median: The middle value when a set of numbers is arranged in order from lowest to highest.
  98. Meta-analysis: A type of project that is statistical combination of the results of multiple studies addressing a similar research question.
  99. Methodology: The plan and procedures used for research.
  100. Mixed-methods research: A study that uses both quantitative (numerical) and qualitative (descriptive) data collection methods.
  101. Mode: The most frequent value in a set of data.
  102. Multiphase iterative design: A mixed-methods design with several phases, where earlier findings guide later phases of the research.
  103. Narrative inquiry: A research method in which stories and experiences of individuals are collected and analyzed.
  104. Null Hypothesis: The statement that there is no difference or relationship between the variables being studied.
  105. Null hypothesis significance testing (NHST): A statistical test used to see how likely it is that research results happened by chance.
  106. Observational measure: A way to measure something by recording what you can see or what’s left behind.
  107. Open access: Research publications freely available online.
  108. Operationalize: To define in such a way to be easily measured.
  109. Ordinal scale: A ranking system where things are ordered but the gaps between them aren’t always the same.
  110. P-value: The probability of observing a result at least as extreme as the one obtained, assuming the null hypothesis is true.
  111. Parsimony: The simplest explanation for something is usually the best.
  112. Participant observation: A research method where you join the people you’re studying in their daily lives.
  113. Peer review: The evaluation of research by other scientists in the field.
  114. Phenomenological study: A research design that focuses on people’s experiences and perspectives.
  115. Phenomenology: A philosophy that studies how people understand the world around them.
  116. Physiological measure: A way to measure something by recording body functions.
  117. Pilot Study: A small-scale study that helps researchers refine their research topic, methods, and goals before conducting a larger project.
  118. Placebo group: A group in a study that gets a fake treatment, like a sugar pill.
  119. Plagiarism: Using someone else’s work without proper citation.
  120. Population: The entire collection of individuals or items of interest in a study.
  121. Postdoctoral Researcher (Postdoc): A recent Ph.D. graduate conducting advanced research under the supervision of senior scientists.
  122. Positivism: A philosophy that believes science can discover absolute truths.
  123. Postpositivism: A philosophy that acknowledges science is limited and may never get the whole picture.
  124. Precision: How consistent repeated measurements are.
  125. Pragmatism: A philosophy that both facts and people’s beliefs are important to study.
  126. Present/present bias: We overestimate how likely something is based on what we can easily recall.
  127. Primary informant: A key person in a study who can provide valuable information.
  128. Principal Investigator (PI): Leads and oversees research projects, secures funding, manages researchers, and is ultimately responsible for the project’s success.
  129. Qualitative data: Data that describes something in words or categories.
  130. Quantitative data: Data that is numerical and can be measured.
  131. Random assignment: Use of random methods to sort subjects.
  132. Random Sampling: A selection process where every member of the population has an equal chance of being chosen for the sample.
  133. Ratio scale: A type of measurement where zero means there’s absolutely nothing of what’s being measured.
  134. Realism: A philosophy that studies both objective truths and people’s beliefs about those truths.
  135. Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables.
  136. Reliability: How consistent the results of a study are.
  137. Replication: Repeating an experiment to confirm its results.
  138. Replication crisis: The difficulty of reproducing certain scientific findings.
  139. Research: Systematic inquiry to discover new knowledge, solve problems, or answer questions.
  140. Research Assistant: Provides administrative and technical support to research projects, often under the direction of a Research Associate or Scientist.
  141. Research Associate: Supports research projects by conducting experiments, collecting and analyzing data, and assisting senior researchers.
  142. Research Consultant: Professional who provides expert advice and assistance on research projects to clients in various sectors
  143. Research Director: Manages a research program or department, setting strategic direction, overseeing budgets, and leading research teams.
  144. Research Engineer: Applies engineering principles to design, develop, and implement research tools and methods.
  145. Research Ethics Board: Similar to an IRB but might not be associated with a university.
  146. Research Fellow: Holds a prestigious and temporary research position with funding to pursue independent research.
  147. Research methodology: The overall plan a researcher uses to conduct a study.
  148. Research Scientist: Conducts independent research, analyzes data, develops methodologies, and contributes to publications.
  149. Research tool: A specific method a researcher uses to gather or analyze data.
  150. Restorying: Reorganizing research data into a story for better understanding.
  151. Restriction of range: In a correlation, a limited range of scores that weakens the results.
  152. Sample: A subset of the population selected for research, representing the larger group.
  153. Scientific method: A process for conducting research, involving forming a question, making a hypothesis, conducting an experiment, analyzing data, and drawing conclusions.
  154. Selection effect: A risk in studies where groups being compared are inherently different.
  155. Self-report measure: A way to measure something by asking people about themselves.
  156. Slope direction: Whether a line on a graph slants up, down, or stays flat.
  157. Snowball sampling: Finding research participants by asking existing participants for referrals.
  158. Spurious association: A link between two things that seems real but disappears when examined in smaller groups.
  159. Standard Deviation: A measure of how spread out the data is from the mean.
  160. Statistical significance: A judgment about how likely a result is due to chance.
  161. Statistical validity: Refers to the accuracy of the inferences drawn from the data.
  162. Stratified Sampling: A sampling method where the population is divided into subgroups (strata) and a sample is drawn from each subgroup.
  163. Strength: How closely data points cluster around a trend line.
  164. Systematic variability: A change in a variable that predictably goes along with different experiment groups.
  165. t test: A statistical test to see if two groups have different averages.
  166. Temporal precedence: A cause must happen before its effect.
  167. Test-retest reliability: A measure that gives the same results when used multiple times.
  168. Theoretical sampling: Choosing data sources to help build a theory.
  169. Theory: An explanation of how things relate to each other.
  170. Three-interview series: A method with three interviews that focus on a person’s life, experiences, and their meaning.
  171. Translational research: Using basic science findings to solve real-world problems.
  172. Treatment group: The group that gets the intervention in an experiment.
  173. Type I error: false positive.
  174. Type II error: false negative.
  175. Unsystematic variability: Random variation in data that isn’t caused by the experiment itself.
  176. Validity: How well a study measures what it claims to measure.
  177. Variable: A factor that can change in an experiment.
  178. Variance: The square of the standard deviation.
  179. Weight of the evidence: The strength of a scientific conclusion based on how many studies support it.
Scroll to Top