EXAM 1 Review Flashcards by Andrei Valientes

What does the mean of a dataset represent?

A) The most frequently occurring value
B) The middle value when arranged in order
C) The average of all values
D) The difference between the highest and lowest values

C) The average of all values

The mean is calculated by summing all measurements and dividing by the number of measurements.

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What is the purpose of standard deviation in statistics?

A) To determine the average
B) To measure the spread of data around the mean
C) To find the maximum value
D) To count the number of observations

B) To measure the spread of data around the mean

Standard deviation quantifies the amount of variation or dispersion in a set of values.

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What is a confidence interval?

A) The range of values that includes all data points
B) A statistical range that estimates the true population parameter
C) The difference between two means
D) A value that indicates the mean of a dataset

B) A statistical range that estimates the true population parameter

A confidence interval provides a range of values where we expect the true population parameter to lie with a certain level of confidence.

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What is the null hypothesis in hypothesis testing?

A) A statement that there is a difference between groups
B) A statement that there is no effect or difference
C) A prediction of the outcome
D) A claim that must be proven

B) A statement that there is no effect or difference

The null hypothesis posits that any observed difference is due to random chance.

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What does the coefficient of variance (CV) measure?

A) The total number of data points
B) The standard deviation as a percentage of the mean
C) The range of the dataset
D) The median of the dataset

B) The standard deviation as a percentage of the mean

CV is a standardized measure of dispersion that allows for comparison between datasets with different units or scales.

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Which statistical test is used to compare the means of two related groups?

A) Independent t-test
B) Paired t-test
C) ANOVA
D) F-test

B) Paired t-test

The paired t-test compares means from the same group at different times or under different conditions.

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What is an outlier?

A) A value that is repeated in the dataset
B) A data point that significantly differs from other observations
C) The average of the dataset
D) The minimum value in the dataset

B) A data point that significantly differs from other observations

Outliers can skew results and may indicate variability or measurement error.

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What is the significance of p-values in hypothesis testing?

A) They show how likely the null hypothesis is true
B) They indicate the probability of obtaining results at least as extreme as the observed results, under the null hypothesis
C) They measure the spread of data
D) They determine the sample size

B) They indicate the probability of obtaining results at least as extreme as the observed results, under the null hypothesis

A lower p-value suggests that the observed result is unlikely under the null hypothesis, leading to its rejection.

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When should Grubb’s test be used?

A) To compare means of two datasets
B) To identify and handle outliers in a dataset
C) To calculate standard deviation
D) To assess the normality of data

B) To identify and handle outliers in a dataset

Grubb’s test is specifically designed to detect outliers in normally distributed data.

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What is the primary goal of calibration in instrumental analysis?

A) To enhance the sensitivity of the instrument
B) To ensure accurate and precise measurements
C) To determine the method’s limit of detection
D) To minimize random errors

B) To ensure accurate and precise measurements

Calibration establishes a relationship between instrument response and known analyte concentrations.

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Which method is commonly used for calibration in quantitative analysis?

A) Standard addition method
B) Titration
C) Spectrophotometry
D) Chromatography

A) Standard addition method

This method involves adding known quantities of analyte to the sample to quantify the concentration in the original mixture.

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What is the purpose of a calibration curve?

A) To determine the limit of detection
B) To visualize the relationship between concentration and response
C) To identify unknown samples
D) To assess instrument drift

B) To visualize the relationship between concentration and response

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Which of the following methods is NOT a calibration technique?

A) External standard method
B) Internal standard method
C) Blank method
D) Qualitative analysis

D) Qualitative analysis

Qualitative analysis is focused on identifying components rather than quantifying them.

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When using internal standards, what is the purpose of adding a known quantity of a standard to the sample?

A) To improve the accuracy of measurements
B) To eliminate systematic errors
C) To account for variations in sample preparation and instrument response
D) To increase the concentration of the analyte

C) To account for variations in sample preparation and instrument response

Internal standards help to correct for inconsistencies in sample analysis.

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What is the role of a blank in instrumental calibration?

A) It serves as a control sample
B) It increases sensitivity
C) It provides a reference point for zeroing the instrument
D) It determines sample purity

C) It provides a reference point for zeroing the instrument

A blank contains all components except the analyte, allowing the instrument to measure only the analyte’s response.

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In the standard addition method, what is added to the sample?

A) An equal volume of solvent
B) A known concentration of analyte
C) A blank
D) An unknown sample

B) A known concentration of analyte

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What defines the limit of detection (LOD)?

A) The maximum concentration that can be measured
B) The lowest concentration of analyte that can be reliably detected
C) The standard deviation of the blank
D) The minimum volume of sample required

B) The lowest concentration of analyte that can be reliably detected

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What does a slope of a calibration curve indicate?

A) The precision of the measurements
B) The sensitivity of the method
C) The limit of detection
D) The accuracy of the method

B) The sensitivity of the method

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Why is it important to perform calibration regularly?

A) To maintain instrument aesthetics
B) To ensure ongoing accuracy and precision of measurements
C) To save time during analysis
D) To reduce costs

B) To ensure ongoing accuracy and precision of measurements

Regular calibration compensates for instrument drift and changes over time, ensuring reliable results.

What is the main purpose of experimental design in research?

A) To collect as much data as possible
B) To establish cause-and-effect relationships
C) To maximize errors
D) To simplify data interpretation

B) To establish cause-and-effect relationships

Proper experimental design helps to isolate variables and determines how they impact the outcome.

Which of the following is a key principle of experimental design?

A) Randomization
B) Repetition
C) Control
D) All of the above

D) All of the above

Randomization, repetition, and control are essential to minimize bias and variability.

What is the role of a control group in an experiment?

A) To increase variability
B) To serve as a benchmark for comparison
C) To confirm the hypothesis
D) To minimize costs

B) To serve as a benchmark for comparison

Control groups are used to compare results against a baseline where the independent variable is not present.

What is a factorial design?

A) An experimental design with one variable
B) A design that considers the effects of multiple factors simultaneously
C) A design with no randomization
D) A design that includes only qualitative data

B) A design that considers the effects of multiple factors simultaneously

Factorial designs allow researchers to evaluate interactions between multiple independent variables.

Which of the following is a disadvantage of using a completely randomized design?

A) It can introduce bias
B) It does not account for variability among subjects
C) It is difficult to analyze
D) It requires more resources

B) It does not account for variability among subjects

Completely randomized designs may overlook systematic differences among subjects, affecting validity.

What is the purpose of blinding in experimental design? A) To ensure the experiment is successful B) To minimize bias from participants or researchers C) To increase the sample size D) To reduce costs

B) To minimize bias from participants or researchers Blinding helps to ensure that expectations do not influence the outcome of the experiment.

What is the significance of sample size in experimental design? A) Larger sample sizes reduce variability and increase the reliability of results B) Sample size has no impact on results C) Smaller sample sizes are always better D) Sample size only affects qualitative data

A) Larger sample sizes reduce variability and increase the reliability of results A larger sample size improves statistical power and the ability to detect true effects.

In experimental design, what is replication? A) Repeating the entire experiment multiple times B) Repeating individual trials within the experiment C) Collecting more data points in the analysis D) Running the same experiment in different locations

B) Repeating individual trials within the experiment Replication helps to assess the variability of results and improve precision.

What is a "confounding variable"? A) A variable that is controlled in the experiment B) A variable that is unrelated to the independent variable C) An extraneous variable that affects the dependent variable D) A variable that is intentionally manipulated

C) An extraneous variable that affects the dependent variable Confounding variables can distort the perceived relationship between the independent and dependent variables.

Why is it important to randomize subjects in an experiment? A) To simplify analysis B) To ensure that every subject has an equal chance of being assigned to any group C) To reduce costs D) To eliminate the need for controls

B) To ensure that every subject has an equal chance of being assigned to any group Randomization helps to eliminate selection bias and ensures that results are generalizable.

What is a single-factor design? A) An experimental design that evaluates the impact of one independent variable B) A design that includes multiple independent variables C) A non-experimental design D) A design focused on qualitative data

A) An experimental design that evaluates the impact of one independent variable Single-factor designs investigate the effects of one variable while controlling for others.

In a single-factor design, what is typically used to compare the means of different groups? A) ANOVA B) Regression analysis C) Chi-square test D) Correlation analysis

A) ANOVA ANOVA is used to compare means of three or more groups to determine if at least one group mean is different.

Which of the following is NOT a characteristic of single-factor designs? A) Random assignment to groups B) Only one independent variable C) Testing multiple dependent variables D) Control of extraneous variables

C) Testing multiple dependent variables Single-factor designs focus on one independent variable and typically one dependent variable.

When analyzing data from a single-factor experiment, what indicates a significant difference? A) A p-value greater than 0.05 B) A p-value less than 0.05 C) No difference in means D) All groups having the same mean

B) A p-value less than 0.05 A p-value below 0.05 typically indicates statistical significance, suggesting a difference between group means.

What is the main limitation of single-factor designs? A) They require a large sample size B) They cannot assess interaction effects C) They are difficult to analyze D) They are time-consuming

B) They cannot assess interaction effects Single-factor designs do not allow for the examination of how multiple independent variables interact.

In a single-factor experiment, how can researchers control for variability? A) By using random sampling B) By increasing the sample size C) By conducting the experiment in a controlled environment D) All of the above

D) All of the above Controlling for variability can be achieved through random sampling, increased sample size, and conducting experiments in controlled settings.

What type of data is most commonly analyzed in single-factor designs? A) Nominal data B) Ordinal data C) Interval or ratio data D) Qualitative data

C) Interval or ratio data Single-factor designs typically analyze numerical data that can be measured on a continuous scale.

Which of the following is a common method of visualizing results in a single-factor design? A) Venn diagram B) Pie chart C) Bar graph D) Scatter plot

C) Bar graph Bar graphs effectively display the means of different groups, making comparisons clear.

In a single-factor experiment, what is the role of the independent variable? A) It is the variable being measured B) It is the variable that is manipulated by the researcher C) It is a constant factor in the experiment D) It is the result of the experiment

B) It is the variable that is manipulated by the researcher The independent variable is the factor that the researcher changes to observe its effect on the dependent variable.

What is the primary objective of a single-factor experiment? A) To observe correlations between variables B) To establish causality between the independent and dependent variables C) To explore qualitative data D) To increase sample size

B) To establish causality between the independent and dependent variables Single-factor experiments aim to determine how changes in one variable affect another.

What is a multi-factorial design? A) An experimental design that examines multiple independent variables B) A design that only tests one variable at a time C) A design focused on qualitative data D) A non-experimental design

A) An experimental design that examines multiple independent variables Multi-factorial designs allow researchers to study the effects of two or more factors simultaneously.

Which statistical analysis is most appropriate for analyzing multi-factorial design data? A) ANOVA B) Regression analysis C) Chi-square test D) T-test

A) ANOVA ANOVA is suitable for comparing means across multiple groups and assessing interactions between factors.

What advantage does a multi-factorial design have over a single-factor design? A) It is simpler to analyze B) It can assess interactions between factors C) It requires fewer subjects D) It eliminates variability

B) It can assess interactions between factors Multi-factorial designs provide insights into how different independent variables interact and influence the dependent variable.

In a multi-factorial design, what does an interaction effect refer to? A) The main effect of each independent variable B) The combined effect of two or more factors on the dependent variable C) The effect of one variable on another D) The variability in the data

B) The combined effect of two or more factors on the dependent variable Interaction effects occur when the impact of one independent variable on the dependent variable depends on the level of another independent variable.

What type of data is typically analyzed in a multi-factorial design? A) Nominal data B) Ordinal data C) Interval or ratio data D) Qualitative data

C) Interval or ratio data Multi-factorial designs often involve numerical data that can be measured on a continuous scale.