Research Methods Quiz

To answer the research question as best as possible.

At the end of the Introduction section.

A scientific hypothesis is a testable statement about a research question, and a statistical hypothesis is a statement about population parameters that can be tested using statistical methods.

<p>Statistical methods are tools used to analyze data and help answer the scientific research question.</p> Signup and view all the answers

<p>Plain language.</p> Signup and view all the answers

<p>A scientific hypothesis is a statement about a research question. It's a testable idea about the answer to this question.</p> Signup and view all the answers

<p>False.</p> Signup and view all the answers

<p>That their results are in service to answering the scientific research question.</p> Signup and view all the answers

<p>The null hypothesis states that any observed signal in a sample is due to random chance and does not exist at the population level.</p> Signup and view all the answers

<p>The alternate hypothesis proposes that the observed signal is not due to random chance and does exist at the population level.</p> Signup and view all the answers

<p>We are evaluating how confident we are of the signal we observe despite the noise in the data.</p> Signup and view all the answers

<p>The larger the signal and the smaller the noise.</p> Signup and view all the answers

<p>Statistical significance alone is not enough.</p> Signup and view all the answers

<p>Ronald Fisher and Jerzy Neyman (with Egon Pearson).</p> Signup and view all the answers

<p>NHST is considered a 'weird mixture' of the methods for statistical inference.</p> Signup and view all the answers

<p>Which approach is better.</p> Signup and view all the answers

<p>Falsification of the null hypothesis.</p> Signup and view all the answers

<p>The impossibility of proving something is true, especially about human behavior.</p> Signup and view all the answers

<p>Assuming something is true and observing the world through this assumption.</p> Signup and view all the answers

<p>NHST has been misapplied for decades.</p> Signup and view all the answers

<p>To approach science in the “normal” and “customary” ways.</p> Signup and view all the answers

<p>The system developed by Neyman-Pearson.</p> Signup and view all the answers

<p>The misapplication of NHST.</p> Signup and view all the answers

<p>It has resulted in a mishmash of ideas that are often misunderstood and misapplied.</p> Signup and view all the answers

<p>That a signal does not exist.</p> Signup and view all the answers

<p>It represents a value calculated from the signal and noise that corresponds to a standard probability distribution T.</p> Signup and view all the answers

<p>The p-value is the probability of observing a value of t, or a greater value, which is calculated from the area under the curve of T.</p> Signup and view all the answers

<p>The probability of observing the sample data given the null hypothesis is true, i.e. P(data|H0).</p> Signup and view all the answers

<p>That the null hypothesis is likely to be wrong.</p> Signup and view all the answers

<p>A small p-value suggests that the assumption of the null hypothesis being true is likely wrong, and that a signal likely exists.</p> Signup and view all the answers

<p>That the observed variable will follow specific patterns according to theoretical probability distributions.</p> Signup and view all the answers

<p>The term 'significant' is applied to p-values that are less than a pre-established threshold, denoted as alpha.</p> Signup and view all the answers

<p>It is easy to misinterpret a p-value and misunderstand what it actually means.</p> Signup and view all the answers

<p>The difference between the mean anxiety scores of the two groups, i.e. $\bar{x}<em>{under} - \bar{x}</em>{PhD}$.</p> Signup and view all the answers

<p>Close to 0.</p> Signup and view all the answers

<p>The main assumption is that the null hypothesis is true.</p> Signup and view all the answers

<p>A normal distribution.</p> Signup and view all the answers

<p>Because no isolated experiment, however significant, can fully demonstrate a natural phenomenon.</p> Signup and view all the answers

<p>The noise, often represented by the standard deviation of observations.</p> Signup and view all the answers

<p>It measures the probability of observing your data assuming the null hypothesis is true and it indicates that the smaller the value, the less likely the data would have occurred, assuming the null.</p> Signup and view all the answers

<p>The null hypothesis states that the average birth weight of babies in the small town is the same as the general average birth weight, or $𝜇_{ST} = 𝜇$.</p> Signup and view all the answers

<p>It's useful because $μ_{ST} - μ$ represents the signal or the difference we are trying to detect.</p> Signup and view all the answers

<p>If the null hypothesis is true, $x̄ - μ$ is expected to be normally distributed around 0.</p> Signup and view all the answers

<p>If $H_0$ is true, then values of $x̄ - μ$ closer to zero are more probable than values further from zero.</p> Signup and view all the answers

<p>It indicates that it is less probable that the null hypothesis is true.</p> Signup and view all the answers

<p>The text also states that if $H_0$ is true, values of $x̄$ have an equal chance of being less than or greater than $μ$.</p> Signup and view all the answers

<p>The purpose is to standardize the difference so that it corresponds to the Z-distribution.</p> Signup and view all the answers

<p>The value $z$ represents the test statistic.</p> Signup and view all the answers

Refers to the importance and implications of research findings.

Questions formulated to guide a scientific study and seek answers.

Proposed explanations or predictions based on scientific research questions.

Formal statements that include the null and alternative hypotheses in statistics.

A statement that posits no effect or no difference in the research context.

The hypothesis that signifies the presence of an effect or a difference.

A type of research focused on quantifying relationships or dependencies between variables.

Techniques employed to analyze data and answer research questions using statistics.

Claims that the observed signal is real and not just random chance.

Assess confidence in observed signals amidst data noise.

The observed effect (signal) versus random variation (noise) in data.

A statistical method using null and alternate hypotheses for inference.

Indicates the likelihood that the observed result is not due to chance alone.

One of the pioneers of methods for statistical inference.

An alternative approach for hypothesis testing developed by Neyman and Pearson.

Null Hypothesis Significance Testing, a method to make inferences about a population based on sample data.

The process of assuming the null hypothesis is true and determining if observations support or contradict this assumption.

A statistic that measures the strength of the evidence against the null hypothesis.

A method of statistical inference focusing on falsifying the null hypothesis rather than proving hypotheses.

A phenomenon where many scientific studies cannot be replicated or reproduced, often linked to NHST misuse.

In NHST, we assume the null hypothesis is true and investigate the likelihood of observations.

Encouragement to question and analyze the practices surrounding NHST due to potential misapplications.

Assumes that no signal exists in the population.

The likelihood of observing data under the null hypothesis.

A p-value approaching 0 indicates evidence against 𝐻0.

Patterns assumed for data under 𝐻0, e.g., normal distribution.

Average anxiety levels calculated for two groups (undergrads, PhDs).

The calculated difference between group anxiety scores (𝑥¯𝑢𝑛𝑑𝑒𝑟 − 𝑥¯𝑃ℎ𝐷 ).

Variability in observations, often measured by standard deviation.

Combining signal and noise to evaluate the null hypothesis.

A calculated value from signal and noise, corresponding to a standard probability distribution T.

A p-value is considered significant if it is less than a predetermined threshold, typically 0.05.

When calculating p-values, we do so under the assumption that the null hypothesis is valid.

A pre-established value (usually 0.05) that determines significance in statistical testing.

A probability distribution with a mean of zero and a standard deviation of one, used in hypothesis testing.

Understanding p-values involves realizing they reflect data probability if H0 holds true, not proof of H0 being false.

A probability distribution that is symmetric about the mean.

A measure of the amount of variation in a set of values.

The difference between sample mean and population mean.

The average weight calculated from the sample of babies.

Unpredictable variations in data measurements.

The process of transforming values to a standard normal distribution.