2.3 Hypothesis formation and testing
5 min read•july 30, 2024
Hypothesis formation and testing are crucial steps in the scientific method. Scientists develop explanations for phenomena based on observations and existing theories, then design experiments to test these ideas. This process helps separate fact from fiction and build our understanding of the world.
Careful experiment design and data analysis are key to drawing valid conclusions. By controlling variables, using statistical techniques, and interpreting results thoughtfully, researchers can evaluate hypotheses and revise theories. This iterative process drives scientific progress and knowledge accumulation over time.
Hypothesis Formulation
Defining and Generating Hypotheses
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- A hypothesis is a proposed explanation for a phenomenon based on limited evidence that can be tested through further investigation and experimentation
- Hypotheses are often formulated by making inductive generalizations from existing observations (patterns in data) or by making deductive predictions from existing theories (logical consequences of assumptions)
- Testable hypotheses must be logically consistent, empirically falsifiable, and sufficiently precise to generate specific predictions
- Operational definitions specify the exact procedures used to measure or manipulate variables in a way that allows hypotheses to be empirically tested (reaction time, self-report questionnaire)
Types of Hypotheses
- Null and alternative hypotheses are mutually exclusive statements about the relationships between variables, where the predicts no effect or relationship
- Null hypothesis: There is no difference in mean scores between the treatment and control groups
- : There is a difference in mean scores between the treatment and control groups
- specify the expected direction of an effect or relationship (, ), while only predict the existence of an effect or relationship without specifying its direction
- propose that changes in one variable cause changes in another variable, while only propose that two variables are related without specifying a causal direction
Experiment Design
Types of Experimental Designs
- systematically manipulate one or more independent variables while holding other variables constant and measuring the effect on one or more dependent variables
- : The variable manipulated by the experimenter (drug dosage)
- : The variable measured by the experimenter (symptom severity)
- : An extraneous variable that varies systematically with the independent variable and affects the dependent variable (age, income)
- of participants or samples to different experimental conditions minimizes the influence of potential confounding variables
- compare different groups of participants exposed to different levels of the independent variable, while expose the same participants to all levels of the independent variable
Controlling for Extraneous Variables
- Placebos and procedures are used to minimize demand characteristics and experimenter bias in interventional studies
- : An inert substance or procedure that mimics the appearance of an active treatment (sugar pill, sham surgery)
- Double-blind: Neither the participants nor the experimenters directly interacting with them know which condition each participant is assigned to
- examine the strength and direction of relationships between measured variables without manipulating them directly
- Positive correlation: Two variables increase or decrease together (height and weight)
- Negative correlation: One variable increases as the other decreases (hours of sleep and fatigue)
- lack random assignment but still attempt to establish cause-and-effect relationships by comparing pre-existing groups (males vs. females) or using interrupted time-series (before vs. after policy change)
Data Analysis and Interpretation
Descriptive and Inferential Statistics
- such as means, standard deviations, and correlations summarize the main features of a dataset
- Mean: The arithmetic average of a set of numbers
- Standard deviation: A measure of the average distance between each data point and the mean
- : A measure of the strength and direction of the linear relationship between two variables (-1 to +1)
- Null hypothesis significance testing uses probability theory to determine the likelihood of obtaining the observed results if the null hypothesis were true
- The represents the probability of obtaining results as extreme or more extreme than those observed, assuming the null hypothesis is true. A p-value below a pre-specified alpha level (e.g., .05) is considered statistically significant
Effect Sizes and Confidence Intervals
- Effect sizes indicate the strength or magnitude of an observed effect or relationship, independent of sample size
- : A standardized measure of the difference between two means in units of standard deviation
- : A measure of the strength and direction of the linear relationship between two variables
- Confidence intervals estimate the range of plausible values for a population parameter based on the variability in a sample statistic
- 95% : The range of values that has a 95% probability of containing the true population parameter
- uses prior probabilities and observed data to calculate the of different hypotheses
- : The probability of a hypothesis being true before observing the data
- Posterior probability: The updated probability of a hypothesis being true after observing the data
Hypothesis Revision
Updating Theories Based on Evidence
- Scientific theories are provisional explanations that can be revised or replaced based on new evidence that contradicts their predictions
- Replication of key findings using similar or different methods is essential for establishing the reliability and generalizability of results
- Hypotheses that are consistently supported by multiple lines of evidence are provisionally accepted, while those that are consistently refuted are rejected or revised
Dealing with Anomalous Results
- Anomalous or unexpected results may suggest the need to modify the scope or boundary conditions of a hypothesis, or to develop entirely new hypotheses
- Scope: The range of phenomena that a hypothesis attempts to explain (animal learning vs. human learning)
- Boundary conditions: The specific circumstances under which a hypothesis is expected to hold true (short-term memory vs. long-term memory)
- Meta-analyses statistically combine the results of multiple studies to provide a more precise estimate of the overall size and consistency of an effect
- Converging evidence from multiple methods (experiments, observations, simulations) and sources of data (self-report, behavioral, physiological) provides the strongest support for a scientific hypothesis or theory
Key Terms to Review (43)
Alternative hypothesis: The alternative hypothesis is a statement that proposes a potential outcome or relationship that differs from the null hypothesis, suggesting that a particular effect or phenomenon exists. It plays a crucial role in hypothesis testing, as it provides a direction for the research and outlines what researchers aim to support through their data analysis.
Antecedent: An antecedent is a condition or premise that must be true for a consequent or outcome to be valid in a logical argument or hypothesis. It plays a critical role in hypothesis formation, as it helps in establishing relationships between variables and determining the necessary conditions for an assertion to hold true.
Associative hypotheses: Associative hypotheses are statements that propose a relationship or correlation between two or more variables, suggesting that changes in one variable are associated with changes in another. These hypotheses are crucial in hypothesis formation and testing as they help guide the research design and data analysis, allowing scientists to explore potential connections and understand underlying patterns in the data.
Bayesian Inference: Bayesian inference is a statistical method that applies Bayes' theorem to update the probability for a hypothesis as more evidence or information becomes available. This approach allows for a flexible understanding of uncertainty, making it useful for drawing conclusions from data and making predictions based on prior knowledge. Bayesian inference plays a significant role in reasoning processes, hypothesis testing, and establishing causal relationships in various scientific fields.
Between-subjects designs: Between-subjects designs are experimental setups where different groups of participants are exposed to different conditions, allowing researchers to compare the effects of these conditions on separate groups. This design is particularly useful in hypothesis testing, as it helps to minimize the potential for carryover effects and ensures that each group's performance is independent of the others. In hypothesis formation, this method allows for clear comparisons between conditions, helping to establish causal relationships.
Causal explanation: A causal explanation is a type of explanation that identifies the cause-and-effect relationships between events or phenomena. It seeks to clarify how one event leads to another, providing insight into the mechanisms and processes at play. This type of explanation is fundamental in understanding scientific inquiry, where establishing causal links is crucial for validating theories and testing hypotheses.
Causal hypotheses: Causal hypotheses are specific predictions that propose a cause-and-effect relationship between two or more variables. They aim to explain how one variable, known as the independent variable, influences another variable, known as the dependent variable. Understanding these hypotheses is crucial for testing theories and advancing knowledge through empirical research.
Cohen's d: Cohen's d is a statistical measure that quantifies the effect size of a treatment or intervention, indicating the standardized difference between two group means. It provides a way to understand the magnitude of an effect beyond just statistical significance, helping researchers gauge practical importance in hypothesis testing.
Confidence interval: A confidence interval is a range of values, derived from a data set, that is likely to contain the true value of an unknown population parameter. This statistical tool provides an estimate of the uncertainty associated with a sample statistic, allowing researchers to make informed inferences about the population being studied. By specifying a confidence level, such as 95% or 99%, the interval indicates the degree of certainty researchers can have that the true parameter lies within this range.
Confirmation Bias: Confirmation bias is the tendency to search for, interpret, favor, and recall information in a way that confirms one’s preexisting beliefs or hypotheses. This cognitive shortcut can significantly influence how scientists conduct research and analyze data, leading them to favor evidence that supports their views while overlooking or dismissing contradictory information.
Confounding Variable: A confounding variable is an extraneous factor that correlates with both the independent variable and the dependent variable in a study, potentially leading to incorrect conclusions about their relationship. These variables can create misleading associations, making it difficult to determine whether changes in the dependent variable are actually caused by the independent variable or by the confounding factor.
Consequent: In logical reasoning and hypothesis testing, a consequent is the outcome or result that follows from a particular premise or hypothesis. It is often paired with an antecedent, which is the condition that leads to the consequent. Understanding the relationship between antecedents and consequents is crucial for formulating hypotheses and making predictions based on experimental data.
Controlled experiments: Controlled experiments are scientific tests where one variable is changed while others are kept constant to determine the effect of the changing variable. This method allows researchers to isolate the cause-and-effect relationships in hypothesis testing, providing clarity on whether a specific change leads to observable outcomes.
Correlation coefficient: The correlation coefficient is a statistical measure that quantifies the degree to which two variables are related. It ranges from -1 to 1, where a value of 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation. This measure is crucial in hypothesis testing as it helps researchers determine the strength and direction of relationships between variables.
Correlational Designs: Correlational designs are research methods used to assess the relationship between two or more variables without manipulating them. This type of design can reveal patterns or associations, allowing researchers to identify whether changes in one variable are related to changes in another, but it does not determine causation. By examining the strength and direction of relationships, these designs help in hypothesis formation and testing by providing insights into potential connections between variables.
Deductive Reasoning: Deductive reasoning is a logical process in which a conclusion is drawn from a set of premises that are assumed to be true. It starts with a general statement or hypothesis and examines the possibilities to reach a specific, logical conclusion. This method is crucial in scientific inquiry as it allows researchers to formulate predictions and test them, making it essential for hypothesis formation and evaluation.
Dependent Variable: A dependent variable is a factor in an experiment that is measured or tested to see how it is affected by changes in another variable, typically the independent variable. It represents the outcome or effect that researchers are interested in understanding as they manipulate the independent variable. The relationship between these two variables is crucial for hypothesis testing, as it helps to determine if a change in one leads to a change in the other.
Descriptive statistics: Descriptive statistics refers to a set of techniques used to summarize and describe the main features of a dataset, providing simple summaries about the sample and the measures. These statistics help researchers to present quantitative descriptions in a manageable form, allowing them to understand patterns and trends in data. By organizing and simplifying large amounts of information, descriptive statistics serve as the foundation for hypothesis formation and testing, providing essential insights before more complex analyses are conducted.
Directional Hypotheses: A directional hypothesis is a specific type of hypothesis that predicts the direction of the relationship between two variables, indicating whether one variable will increase or decrease as the other variable changes. This type of hypothesis is important because it provides a clear expectation for research outcomes, allowing researchers to test for specific effects rather than simply assessing whether a relationship exists.
Double-blind: A double-blind study is a research design in which neither the participants nor the researchers know who is receiving the treatment or the placebo. This method is crucial for reducing bias in both the administration of the treatment and the reporting of outcomes, ensuring that the results are more reliable and valid.
Effect Size: Effect size is a quantitative measure that reflects the magnitude of a phenomenon or the strength of a relationship between variables. It provides context to statistical significance by indicating how large an effect is in practical terms, which helps in understanding the real-world relevance of research findings, especially in hypothesis testing.
Empiricism: Empiricism is the philosophical standpoint that emphasizes the role of sensory experience in the formation of knowledge, asserting that knowledge is primarily derived from observation and experimentation. This approach connects closely with the practices of scientific inquiry, highlighting the importance of empirical evidence in validating theories and claims.
Falsifiability: Falsifiability is the principle that for a theory to be considered scientific, it must be able to be tested and potentially disproven by empirical evidence. This concept emphasizes the importance of observation and experimentation in science, ensuring that claims can be challenged and evaluated through rigorous methods.
Hypothetico-deductive model: The hypothetico-deductive model is a scientific method that involves forming hypotheses and then testing them through deductive reasoning to confirm or refute predictions. This approach helps scientists navigate the problem of induction by establishing a systematic way to derive testable predictions from theories, thereby linking hypothesis formation with empirical evidence and confirmation processes.
Independent Variable: An independent variable is a factor or condition that is manipulated or changed in an experiment to observe its effects on a dependent variable. This variable is crucial in hypothesis formation and testing, as it allows researchers to establish cause-and-effect relationships between different variables in a study.
Inferential statistics: Inferential statistics is a branch of statistics that involves making predictions or inferences about a population based on a sample of data drawn from that population. This method allows researchers to estimate population parameters and test hypotheses, providing insights beyond the immediate data collected.
Karl Popper: Karl Popper was a 20th-century philosopher of science known for his contributions to the philosophy of science and the principle of falsifiability. His work challenged the traditional views of scientific method, advocating that scientific theories should be tested and potentially refuted rather than confirmed, emphasizing the dynamic nature of scientific inquiry.
Mechanistic explanation: A mechanistic explanation is a type of scientific explanation that accounts for phenomena by detailing the underlying mechanisms or processes that produce them. This approach emphasizes the interactions and components involved in creating specific outcomes, often likening complex systems to machines where each part has a defined role. By focusing on these mechanisms, this explanation seeks to provide a clearer understanding of both deterministic and probabilistic relationships within scientific inquiry and hypothesis testing.
Negative Correlation: Negative correlation refers to a statistical relationship between two variables in which an increase in one variable leads to a decrease in the other. This concept is crucial when forming and testing hypotheses, as it helps to identify and understand the direction of relationships between different factors.
Non-directional hypotheses: Non-directional hypotheses are statements predicting that there will be a difference or relationship between variables, but without specifying the direction of that difference. This means that the hypothesis does not indicate whether one variable will increase or decrease relative to another, allowing for any potential outcome. This type of hypothesis is often used in research when there is limited prior knowledge about the expected direction of the relationship.
Null hypothesis: The null hypothesis is a fundamental concept in statistical hypothesis testing that proposes there is no effect or no difference in a given situation. It serves as the default assumption that any observed effect or difference in data is due to random chance rather than a true effect, establishing a basis for comparison when evaluating evidence against it.
Operationalization: Operationalization is the process of defining and measuring a concept or variable in a way that allows it to be empirically tested. This involves translating abstract theoretical concepts into specific, observable, and quantifiable elements that can be analyzed through research methods. It is essential in hypothesis formation and testing as it provides clarity and specificity to what is being studied, ensuring that research can be replicated and validated.
P-value: A p-value is a statistical measure that helps scientists determine the significance of their research results. It represents the probability of observing the data, or something more extreme, if the null hypothesis is true. In hypothesis testing, a smaller p-value indicates stronger evidence against the null hypothesis, guiding researchers in making decisions about their hypotheses.
Pearson's r: Pearson's r, also known as the Pearson correlation coefficient, is a statistical measure that calculates the strength and direction of the linear relationship between two continuous variables. This coefficient ranges from -1 to 1, where -1 indicates a perfect negative correlation, 1 indicates a perfect positive correlation, and 0 indicates no correlation at all. It plays a critical role in hypothesis testing by providing evidence for or against the existence of a relationship between variables.
Placebo: A placebo is a substance or treatment with no active therapeutic effect, often used in clinical trials as a control to test the efficacy of new medications. The placebo effect occurs when a patient experiences real improvements in their condition after receiving a placebo due to their expectations or beliefs about the treatment, rather than the treatment itself. Understanding placebos is crucial in hypothesis formation and testing, as they help researchers isolate the effects of the actual intervention from psychological factors.
Positive correlation: Positive correlation refers to a statistical relationship between two variables in which both variables move in the same direction; as one variable increases, the other variable also tends to increase. This relationship is significant in hypothesis formation and testing, as it helps researchers understand and predict how changes in one variable may influence another.
Posterior probability: Posterior probability is the probability of a hypothesis being true after observing new evidence, calculated using Bayes' theorem. It combines prior probability, which reflects initial beliefs about the hypothesis, with the likelihood of the observed evidence given that hypothesis. This concept is vital for understanding how scientific knowledge evolves with new data, making it essential in hypothesis formation and testing.
Prior Probability: Prior probability is the initial estimation of the likelihood of a hypothesis being true before any evidence is taken into account. It serves as a foundational component in Bayesian inference, where it gets updated with new evidence to produce a posterior probability, reflecting our revised beliefs. Understanding prior probability is essential for hypothesis formation and testing, as it influences how we interpret new data and draw conclusions.
Quasi-experimental designs: Quasi-experimental designs are research methods that allow for the investigation of causal relationships without the use of random assignment to groups. They typically involve comparing groups that have already been formed, such as existing classes or communities, which helps in understanding how an intervention or treatment impacts outcomes. These designs are important in situations where true experiments are not feasible due to ethical, logistical, or practical constraints.
Random assignment: Random assignment is a method used in experimental research to assign participants to different groups or conditions by chance, ensuring that each participant has an equal opportunity of being placed in any group. This technique helps eliminate biases and ensures that the groups are comparable at the start of the experiment, allowing for valid conclusions about the effects of the independent variable on the dependent variable.
Statistical Significance: Statistical significance refers to the likelihood that a relationship between two or more variables is caused by something other than random chance. It helps researchers determine if their findings are meaningful and can be generalized to a larger population. This concept is essential in hypothesis testing, where researchers use statistical tests to evaluate whether their observed results support or refute the initial hypotheses.
Thomas Kuhn: Thomas Kuhn was an influential philosopher of science known for his concept of 'paradigm shifts,' which describe fundamental changes in scientific thought and practice. His work highlights the importance of historical context in science, illustrating how scientific progress does not occur linearly but through revolutions that redefine the frameworks within which scientists operate.
Within-subjects designs: Within-subjects designs are experimental setups where the same participants are exposed to all conditions of the experiment. This approach allows researchers to control for individual differences by comparing participants' responses across different conditions, which can enhance the sensitivity of the statistical analyses and help isolate the effects of the independent variable.