Hypothesis evaluation
55 flashcards covering Hypothesis evaluation for the ACT Science section.
Hypothesis evaluation is the process of testing a scientific idea, called a hypothesis, to see if it's supported by evidence. A hypothesis is basically an educated guess about how something in the natural world works, like predicting that a certain factor affects plant growth. By examining data from experiments or observations, you determine whether the hypothesis holds true or needs adjustment. This skill is essential in science because it ensures ideas are based on solid evidence rather than assumptions.
On the ACT Science section, hypothesis evaluation appears in questions that require you to analyze experiments, data tables, graphs, or research summaries. You'll often need to decide if a hypothesis is supported, refuted, or inconclusive based on the evidence provided. Common traps include mistaking correlation for causation or overlooking experimental flaws, like unmentioned variables. Focus on identifying key evidence, such as trends in data or control groups, to make accurate conclusions.
Always compare the hypothesis predictions directly with the experimental results.
Terms (55)
- 01
Hypothesis
A hypothesis is a testable statement or prediction that explains an observed phenomenon and can be supported or refuted through experimentation.
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Null Hypothesis
The null hypothesis is a statement that assumes no effect or no relationship between variables, and it is used as a starting point to test if data provides evidence against it.
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Alternative Hypothesis
The alternative hypothesis is a statement that proposes an effect or relationship between variables, and it is accepted if evidence contradicts the null hypothesis.
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Independent Variable
The independent variable is the factor that is deliberately changed in an experiment to observe its effect on the outcome.
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Dependent Variable
The dependent variable is the factor that is measured or observed in an experiment, as it may change in response to the independent variable.
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Controlled Variable
A controlled variable is a factor kept constant throughout an experiment to ensure that only the independent variable affects the dependent variable.
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Control Group
The control group is a subset of subjects in an experiment that does not receive the treatment, allowing comparison to the experimental group to assess the treatment's effect.
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Experimental Group
The experimental group is a subset of subjects that receives the treatment or condition being tested, while other factors are kept the same as in the control group.
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Scientific Method
The scientific method is a systematic process involving observation, hypothesis formation, experimentation, data analysis, and conclusion to investigate natural phenomena.
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Observation
An observation is a direct or indirect gathering of information about the natural world using the senses or instruments, which can lead to hypothesis formation.
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Inference
An inference is a conclusion drawn from evidence and reasoning, often extending beyond direct observations to explain patterns or relationships.
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Data
Data are the pieces of information collected during an experiment, such as measurements or observations, which are used to evaluate a hypothesis.
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Sample Size
Sample size refers to the number of subjects or items included in an experiment, as a larger sample can provide more reliable results for hypothesis evaluation.
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Replication
Replication is the process of repeating an experiment multiple times or in different settings to verify results and strengthen the evaluation of a hypothesis.
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Validity
Validity in an experiment means that the study accurately measures what it intends to measure, ensuring that conclusions about the hypothesis are trustworthy.
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Reliability
Reliability refers to the consistency of results when an experiment is repeated, which is crucial for confidently evaluating a hypothesis.
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Bias
Bias is a systematic error in an experiment that skews results, such as researcher prejudice, and must be minimized to fairly evaluate a hypothesis.
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Correlation
Correlation is a statistical relationship between two variables, where changes in one are associated with changes in the other, but it does not imply causation.
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Causation
Causation occurs when one variable directly influences another, and establishing it requires controlled experiments beyond just observing correlation.
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Confounding Variable
A confounding variable is an external factor that influences both the independent and dependent variables, potentially misleading the evaluation of a hypothesis.
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Randomization
Randomization is the process of assigning subjects to groups randomly to reduce bias and ensure a fair test of the hypothesis.
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Double-Blind Study
A double-blind study is an experimental design where neither the subjects nor the researchers know who receives the treatment, minimizing bias in hypothesis evaluation.
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Placebo Effect
The placebo effect is when subjects experience changes due to their belief in a treatment rather than the treatment itself, which can confound hypothesis testing.
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Interpreting Graphs
Interpreting graphs involves analyzing visual data representations to identify trends, patterns, or relationships that support or refute a hypothesis.
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Line Graph
A line graph displays data points connected by lines to show changes over time, helping evaluate hypotheses about trends or rates of change.
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Bar Graph
A bar graph uses bars to compare quantities across categories, aiding in the evaluation of hypotheses about differences between groups.
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Scatter Plot
A scatter plot shows the relationship between two variables as points on a grid, allowing assessment of correlations relevant to a hypothesis.
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Trend Line
A trend line is a straight or curved line on a graph that best fits the data points, used to predict outcomes and evaluate the strength of a hypothesis.
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Outlier
An outlier is a data point that differs significantly from others, which may indicate errors or exceptions that affect hypothesis evaluation.
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Mean
The mean is the average of a set of numbers, calculated by summing them and dividing by the count, and it helps in summarizing data for hypothesis testing.
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Median
The median is the middle value in a sorted list of numbers, providing a measure of central tendency that is less affected by outliers in hypothesis evaluation.
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Mode
The mode is the most frequently occurring value in a data set, useful for identifying common patterns when evaluating a hypothesis.
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Standard Deviation
Standard deviation measures the spread of data points from the mean, indicating data variability that can influence the reliability of hypothesis conclusions.
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Hypothesis Testing Process
The hypothesis testing process involves stating a hypothesis, designing an experiment, collecting data, analyzing results, and drawing conclusions based on evidence.
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Falsifiability
Falsifiability means a hypothesis can be proven wrong through evidence, a key criterion for scientific validity in evaluating claims.
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Evaluating Scientific Claims
Evaluating scientific claims requires examining evidence, methodology, and logical reasoning to determine if a hypothesis is supported.
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Peer Review
Peer review is the process where experts evaluate research before publication, ensuring the hypothesis and methods are sound and credible.
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Strategy for Identifying Variables
A strategy for identifying variables involves clearly defining what is being manipulated and measured to ensure accurate hypothesis evaluation in experiments.
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Common Trap: Confusing Correlation and Causation
Confusing correlation and causation is a common trap where one assumes a relationship implies cause, leading to incorrect conclusions about a hypothesis.
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Common Trap: Small Sample Size
Relying on a small sample size is a trap that can make results unreliable, as it may not represent the population and weaken hypothesis evaluation.
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Common Trap: Uncontrolled Variables
Failing to control variables is a trap that introduces confounding factors, invalidating the ability to fairly evaluate a hypothesis.
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Worked Example: Testing Plant Growth Hypothesis
In a worked example, if a hypothesis states that more sunlight increases plant growth, an experiment measures growth under different light levels while controlling water and soil.
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Statistical Significance
Statistical significance indicates that results are unlikely due to chance, helping determine if data supports a hypothesis at a certain confidence level.
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Type I Error
A Type I error occurs when a true null hypothesis is rejected, such as concluding an effect exists when it does not, affecting hypothesis evaluation.
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Type II Error
A Type II error happens when a false null hypothesis is not rejected, meaning a real effect is missed, which can undermine accurate hypothesis assessment.
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P-Value
The p-value is the probability of obtaining results as extreme as observed, assuming the null hypothesis is true, and it helps decide whether to reject it.
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Data Trends in Experiments
Data trends in experiments refer to patterns like increases or decreases that provide evidence for or against a hypothesis when analyzed.
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Hypothesis Refinement
Hypothesis refinement involves modifying a hypothesis based on initial results to better fit the data, improving subsequent evaluations.
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Experimental Controls
Experimental controls are measures taken to eliminate alternative explanations, ensuring that any observed effects are due to the tested hypothesis.
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Graph Scale Effects
Graph scale effects occur when the way a graph is scaled distorts data interpretation, potentially misleading hypothesis evaluation.
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Anomalous Data
Anomalous data are unexpected results that may indicate errors or new phenomena, requiring careful consideration in hypothesis testing.
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Predictive Power of Hypotheses
The predictive power of hypotheses is their ability to forecast outcomes accurately, which is a key factor in evaluating their validity.
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Evidence-Based Conclusions
Evidence-based conclusions are drawn from analyzed data, ensuring that decisions about a hypothesis are supported by empirical results.
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Bias in Data Collection
Bias in data collection arises from flawed methods, such as leading questions, which can skew results and compromise hypothesis evaluation.
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Reproducibility
Reproducibility is the ability to obtain the same results with repeated experiments, essential for confirming the reliability of a hypothesis.