P1 Stroop
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The independent variable in the Stroop Effect experiment is whether the color word matches or mismatches the color of the ink. The dependent variable is reaction time. It is hypothesized that reaction time will be lower for color-word matches than mismatches. Descriptive statistics on the dataset show higher mean and variability for the incongruent condition. A t-test was conducted and found a statistically significant difference between conditions, rejecting the null hypothesis. This matches expectations that it takes longer to name a color when it mismatches the written word.
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![Stroop Effect P1
Q: What is our independent variable? What is our dependent variable?
A: The independent variable is the two forms of the test either color matching word or color
mismatching word. The dependent variable is reaction time between the task and response.[1]
Q: What is an appropriate set of hypotheses for this task? What kind of statistical test do you
expect to perform? Justify your choices.
A: Two hypotheses are most likely to occur. Either there will be no too little effect in the test
results creating a null hypothesis. However, an alternate hypothesis may show results that color
matching word has a lower response time than color mismatching word.[1]
Indicating that the
incongruent’s reaction time data is significantly higher than the congruent’s reaction time data.
• Null Hypothesis H0 = The data between the two results will not differ significantly. In
which the t-statistic do not differ from alpha of 0.05. In which Ho : µ1 = µ2
[7]
• Alternative Hypothesis H1 = There will be a non-random significant difference between
the data of the two results. In which the t-statistic differ from alpha of 0.05. In which H1 :
µ1 = µ2
[7]
• µ1 is the mean of population 1 and µ2 is the mean of population 2.
We can use a two sample T-Test considering the sample size is less than 30 and the standard
deviation of the population is unknown.[6]
Also both populations are independent for data, but
each person has two measurements.[5]
Q: Report some descriptive statistics regarding this dataset. Include at least one measure of
central tendency and at least one measure of variability.
A:
Congruent Incongruent
Mean 14.05 22.02
Mode N/A N/A
Median 14.36 21.02
Variance 12.67 23.01
Std.Deviation 3.56 4.80
IQR 4.31 5.33
Range 13.70 8.84
Q: Provide one or two visualizations that show the distribution of the sample data. Write one or
two sentences noting what you observe about the plot or plots.
Fig 1: Statistical analysis of
Congruent and Incongruent
Stroop data.](https://image.slidesharecdn.com/19d6fc31-cf57-4d50-bdf6-52f269b0ff3c-170118142722/85/P1-Stroop-1-320.jpg)
![.
We can see from all three figures that on average the reaction time when performing
congruent test is lower than that of the incongruent test for the subjects. From figure 2 with
removal of the outlier using the IQR range we can see the congruent curve is positively skewed,
while the incongruent curve is more of a normal distribution. This might be indicative that some
0
2
4
6
8
0 4 8 12 16 20 24 28 32 36 40
Frequency
Reaction Time (seconds)
Congruent Vs. Incongruent
Without Ourliers
Congruent
Incongruent
0
2
4
6
8
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Frequency
Reaction Time (s)
Congruent Vs. Incongruent
Without Ourliers
Congruent
Incongruent
0.00
5.00
10.00
15.00
20.00
25.00
Congruent Incongruent
ReactionTime(S)
Average Reaction Time
Congruent Vs. Incongruent
Fig 2: Congruent Vs
Incongruent histogram with
outliers.[2]
Fig 3: Congruent Vs
Incongruent histogram
without outliers.[2]
Fig 4: Average reaction time
of Congruent Vs Incongruent](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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The document is an outline for a presentation on mental retardation. It defines mental retardation as a developmental disability that appears in children under 18 and involves an intellectual functioning level. It then lists sections that will discuss the prevalence, causes from prenatal to postnatal factors, classifications from mild to profound, learning characteristics, early intervention, teaching schemes, a video clip, and references.
splashmedia-BarLouie-PokemonGO
Bar Louie hired Splash Media to leverage the popularity of Pokémon GO and drive customers to their locations. Splash Media's strategy involved using geo-located PokéStops and gyms near Bar Louie locations to attract players of the game. They organized Pokémon-themed events like Pokébrawls and happy hours to encourage trainers to spend time and money at the bars. The campaign was successful, generating over $250,000 in additional sales during its first weekend compared to the previous year. Subsequent weeks saw sales increases of $2,000-4,000 on average at key locations.
marketing plsn
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Signia Siemens Hearing Aid Product Portfolio
Gurgaon Hearing Aids Center was founded with the aim to improve the quality of life of people who are unable to hear either due to any type of hearing impairment. Our experienced audiologists and hearing consultants are able to carry out proper diagnosis and treatment for any kind of hearing loss and speech problems.
At Gurgaon Hearing Aids Center, we ensure that the people of Gurgaon have access to professional, independent and affordable hearing care treatment. Being proudly independent, we always identify the best treatments for our clients from the full range of available solutions - including hearing testing, rehabilitation, counseling and hearing aids from all leading manufacturers.
IIC (Invisible in Canal), CIC (Completely in Canal), BTE (Behind the Ear), ITE (In the Ear), RIC (Receiver in Canal), ITC (In the Canal) and latest Digital Hearing Aids are some of the types of hearing aids that you can chose from with us at Gurgaon Hearing Aids Center. PHONAK, SIEMENS, WIDEX, STARKEY and RESOUND are some of the best brands that we offer through our center.
OUR SERVICES
Hearing test for adults and children
Hearing aid consultation and counseling
Hearing Aid Accessories and Batteries
Hearing aids for every need, lifestyle and budget
Hearing aid servicing for various makes and models
Advice on hearing problems, hearing protection and conservation
Hearing aid batteries and dehumidifiers for ear molds
Varias.modelo2.castillala manchalen
Este documento presenta una unidad de evaluación sobre la lectura comprensiva del fragmento de la obra "Los Girasoles Ciegos". La unidad contiene un escenario, 12 tareas con preguntas abiertas y de selección múltiple, e indicadores y criterios de evaluación de competencias lingüísticas y emocionales. El alumno tiene 45 minutos para leer el texto y anexo de información y responder las tareas.
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Envolver pacientes e cuidadores- Tópico 8_Guia curricular da OMS
Objetivos pedagógicos: Compreender as formas nas quais paciente e cuidadores
podem ser envolvidos como parceiros nos cuidados à
saúde, tanto prevenindo um dano quanto aprendendo com
um evento adverso.
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Envolver pacientes e cuidadores- Tópico 8_Guia curricular da OMS
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Analyzing experimental research data
Researchers use several tools and procedures for analyzing quantitative data obtained from different types of experimental designs. Different designs call for different methods of analysis. This presentation focuses on:
T-test
Analysis of variance (F-test), and
Chi-square test
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS11 .docx
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS
11: MEDIA LIBRARY
Premium Videos
Core Concepts in Stats Video
· Testing the Difference Between Two Sample Means
Lightboard Lecture Video
· Independent t Tests
Time to Practice Video
· Chapter 11: Problem 5
Difficulty Scale
(A little longer than the previous chapter but basically the same kind of procedures and very similar questions. Not too hard, but you have to pay attention.)
WHAT YOU WILL LEARN IN THIS CHAPTER
· Using the t test for independent means when appropriate
· Computing the observed t value
· Interpreting the t value and understanding what it means
· Computing the effect size for a t test for independent means
INTRODUCTION TO THE T TEST FOR INDEPENDENT SAMPLES
Even though eating disorders are recognized for their seriousness, little research has been done that compares the prevalence and intensity of symptoms across different cultures. John P. Sjostedt, John F. Schumaker, and S. S. Nathawat undertook this comparison with groups of 297 Australian and 249 Indian university students. Each student was measured on the Eating Attitudes Test and the Goldfarb Fear of Fat Scale. High scores on both measures indicate the presence of an eating disorder. The groups’ scores were compared with one another. On a comparison of means between the Indian and the Australian participants, Indian students scored higher on both of the tests, and this was due mainly to the scores of women. The results for the Eating Attitudes Test were t(544) = −4.19, p < .0001, and the results for the Goldfarb Fear of Fat Scale were t(544) = −7.64, p < .0001.
Now just what does all this mean? Read on.
Why was the t test for independent means used? Sjostedt and his colleagues were interested in finding out whether there was a difference in the average scores of one (or more) variable(s) between the two groups. The t test is called independent because the two groups were not related in any way. Each participant in the study was tested only once. The researchers applied a t test for independent means, arriving at the conclusion that for each of the outcome variables, the differences between the two groups were significant at or beyond the .0001 level. Such a small chance of a Type I error means that there is very little probability that the difference in scores between the two groups was due to chance and not something like group membership, in this case representing nationality, culture, or ethnicity.
Want to know more? Go online or to the library and find …
Sjostedt, J. P., Schumaker, J. F., & Nathawat, S. S. (1998). Eating disorders among Indian and Australian university students. Journal of Social Psychology, 138(3), 351–357.
LIGHTBOARD LECTURE VIDEO
Independent t Tests
THE PATH TO WISDOM AND KNOWLEDGE
Here’s how you can use Figure 11.1, the flowchart introduced in Chapter 9, to select the appropriate test statistic, the t test for independent means. Follow along the highlighted sequence of steps in Figure 1.
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS11 .docx
A study compared eating disorder symptoms between 297 Australian and 249 Indian university students using the Eating Attitudes Test and Goldfarb Fear of Fat Scale. Indian students scored higher on both tests, especially women. Statistical analysis found the differences were highly significant (p < .0001) between the groups. However, the small effect size (-0.14) suggests the actual magnitude of the difference between memory technique groups was likely small.
HYPOTHESES.pptx
The document discusses hypotheses in research. A hypothesis is a testable statement about the relationship between two variables. Researchers propose a null hypothesis, which states there is no relationship between the variables, and an alternative or experimental hypothesis, which predicts a relationship. Statistical tests are used to analyze data and determine whether to reject the null hypothesis in favor of the alternative hypothesis. The document provides examples of different types of hypotheses and statistical tests used, including t-tests and z-tests.
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Here are my responses to the guide questions:
1. I decided to teach in SHS because I wanted to help guide students in their transition to college and career. I find it rewarding to support students' personal and academic growth during this important stage of their lives.
2. Two of the most significant experiences I've had teaching Research involve seeing students get excited about their topics and taking ownership of their work. It's amazing to see their eyes light up when they discover something interesting during the research process. I also appreciate witnessing students' confidence grow as they learn to independently plan and conduct research. These experiences are meaningful because they show the positive impact of research skills on student learning and development.
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This document provides an overview of a presentation on statistical hypothesis testing using the t-test. It discusses what a t-test is, how to perform a t-test, and provides an example of a t-test comparing spelling test scores of two groups that received different teaching strategies. The document outlines the six steps for conducting statistical hypothesis testing using a t-test: 1) stating the hypotheses, 2) choosing the significance level, 3) determining the critical values, 4) calculating the test statistic, 5) comparing the test statistic to the critical values, and 6) writing a conclusion.
Marketing Research Hypothesis Testing.pptx
This document provides an overview of parametric and non-parametric hypothesis tests. It defines parametric tests as those that assume an underlying normal distribution, and lists common parametric tests like the z-test, t-test, F-test, and ANOVA. Non-parametric tests make no distributional assumptions and common examples discussed include the Mann-Whitney U test, chi-square test, and Kruskal-Wallis test. The document provides details on assumptions and procedures for conducting each of these important statistical hypothesis tests.
Directions The purpose of Project 8 is to prepare you for the final.docx
Directions: The purpose of Project 8 is to prepare you for the final, comprehensive exam and is set up EXACTLY the same. Questions 1 and 2 are not graded in this exercise, but are on the final. Be sure to answer them still so you can receive feedback. Once done with these, move into the calculation questions.
Be advised that you will need to decide which type of test to use in most of the problems. Please write out all pertinent information for each of the 4 steps of hypothesis testing. For the calculations, you only need to provide the values of all statistics for that test. There is no need to show work.
List the four Steps of the Hypothesis test:
Step 1 –
Step 2 –
Step 3 –
Step 4 –
This semester we have discussed the following statistical analyses.
Z-test
One-Sample
t
-test
Independent Groups
t
-test
Repeated Measures
t
-test
One-Way ANOVA
Repeated Measures ANOVA
Correlation
When do you use them? Please type your answer in the Test Used column.
ơ is given
µ is given
Groups Compared
Test Used
No
No
Looks at the same group at 2 different times or across two different conditions
Yes
Yes
Sample against population
Examines the degree to which two variables relate to one another
No
No
Looks at the same group at 2 or more times or across 2 or more conditions
No
No
Examines mean differences between two different groups
No
Yes
Sample against population
No
No
Examines mean differences between 2 or more groups
1. A researcher for a cereal company wanted to demonstrate the health benefits of eating oatmeal. A sample of 9 volunteers was obtained and each participant ate a fixed diet without any oatmeal for 30 days. At the end of the 30-day period, cholesterol was measured for each individual. Then the participants began a second 30-day period in which they repeated exactly the same diet except that they added 2 cups of oatmeal each day. After the second 30-day period, cholesterol levels were measured again and the researcher recorded the difference between the two scores for each participant. For this sample, cholesterol scores average M = 16 points lower with the oatmeal diet with SS = 538 for the difference scores.
10 points
·
Are the data sufficient to indicate a significant change in cholesterol level? Use a two-tailed test with α = .01.
·
Compute r
2
to measure the size of the treatment effect.
2. One possible explanation for why some birds migrate and others maintain year round residency in a single location is intelligence. Specifically, birds with smaller brain, relative to their body size, are not simply smart enough to find food during the winter and must migrate to warmer climates where food is easily available. Birds with bigger brains, on the other hand, are more creative and can find food even when the weather turns harsh. Following are hypothetical data similar to the actual results. The numbers represent relative brain size for the individual birds in each sample.
10 points
Non-Migrating
S.
Data analysis
The document discusses different measures of central tendency (mean, median, mode) and how to determine which is most appropriate based on the type of data. It also covers measures of dispersion like range, standard deviation, and variance which provide information about how spread out values are from the central point. The mean is the most commonly used measure of central tendency but the median is less affected by outliers, while the mode represents the most frequent value.
© 2014 Laureate Education, Inc. Page 1 of 5 Week 4 A.docx
© 2014 Laureate Education, Inc. Page 1 of 5
Week 4: A Short Course in Statistics Handout
This information was prepared to call your attention to some basic concepts underlying
statistical procedures and to illustrate what types of research questions can be
addressed by different statistical tests. You may not fully understand these tests without
further study. However, you are strongly encouraged to note distinctions related to type
of measurement used in gathering data and the choice of statistical tests. Feel free to
post questions in the “Contact the Instructor” section of the course.
Statistical symbols:
µ mu (population mean)
α alpha (degree of error acceptable for incorrectly rejecting the null hypothesis,
probability that results are unlikely to occur by chance)
≠ (not equal)
≥ (greater than or equal to)
≤ less than or equal to)
ᴦ (sample correlation)
ρ rho (population correlation)
t r (t score)
z (standard score based on standard deviation)
χ
2
Chi square (statistical test for variables that are not interval or ratio scale, (i.e.
nominal or ordinal))
p (probability that results are due to chance)
Descriptives:
Descriptives are statistical tests that summarize a data set.
They include calculations of measures of central tendency (mean, median, and mode),
and dispersion (e.g., standard deviation and range).
Note: The measures of central tendency depend on the measurement level of the
variable (nominal, ordinal, interval, or ratio). If you do not recall the definitions for these
levels of measurement, see
http://www.ats.ucla.edu/stat/mult_pkg/whatstat/nominal_ordinal_interval.htm
You can only calculate a mean and standard deviation for interval or ratio scale
variables.
For nominal or ordinal variables, you can examine the frequency of responses. For
example, you can calculate the percentage of participants who are male and female; or
the percentage of survey respondents who are in favor, against, or undecided.
Often nominal data is recorded with numbers, e.g. male=1, female=2. Sometimes
people are tempted to calculate a mean using these coding numbers. But that would be
http://www.ats.ucla.edu/stat/mult_pkg/whatstat/nominal_ordinal_interval.htm
© 2014 Laureate Education, Inc. Page 2 of 5
meaningless. Many questionnaires (even course evaluations) use a likert scale to
represent attitudes along a continuum (e.g. Strongly like … Strongly dislike). These too
are often assigned a number for data entry, e.g. 1–5. Suppose that most of the
responses were in the middle of a scale (3 on a scale of 1–5). A researcher could
observe that the mode is 3, but it would not be reasonable to say that the average
(mean) is 3 unless there were exact differences between 1 and 2, 2 and 3 etc. The
numbers on a scale such as this are ordered from low to high or high to low, but there is
no way to say that there is a quantifiably equal difference between each of the choices.
.
Applied statistics lecture_3
This document provides an introduction to applied statistics and statistical methods. It discusses objectives such as going beyond the mean and tests of differences like t-tests and ANOVA. It also covers descriptive statistics such as measures of central tendency and dispersion, inferential statistics like z-scores and confidence intervals, and tests of differences including independent and paired t-tests and nonparametric alternatives.
non para.doc
This document discusses non-parametric tests and how to use them to compare groups when assumptions of parametric tests are violated. It explains that non-parametric tests like the Wilcoxon and Kruskal-Wallis tests can be used when samples are small or data is not normally distributed. The Kruskal-Wallis test allows comparison of more than two groups by ranking all data and comparing mean ranks between groups. An example compares student grades under different teaching methods using both Kruskal-Wallis and ANOVA tests.
Assessment 3 – Hypothesis, Effect Size, Power, and t Tests.docx
Assessment 3 – Hypothesis, Effect Size, Power, and
t
Tests
Complete the following problems within this Word document. Do not submit other files. Show your work for problem sets that require calculations. Ensure that your answer to each problem is clearly visible. You may want to highlight your answer or use a different type color to set it apart.
Hypothesis, Effect Size, and Power
Problem Set 3.1: Sampling Distribution of the Mean Exercise
Criterion:
Interpret population mean and variance.
Instructions:
Read the information below and answer the questions.
Suppose a researcher wants to learn more about the mean attention span of individuals in some hypothetical population. The researcher cites that the attention span (the time in minutes attending to some task) in this population is normally distributed with the following characteristics: 20
36
. Based on the parameters given in this example, answer the following questions:
1. What is the population mean (μ)? __________________________
2. What is the population variance
? __________________________
3. Sketch the distribution of this population. Make sure you draw the shape of the distribution and label the mean plus and minus three standard deviations.
Problem Set 3.2: Effect Size and Power
Criterion:
Explain effect size and power.
Instructions:
Read each of the following three scenarios and answer the questions.
Two researchers make a test concerning the effectiveness of a drug use treatment. Researcher A determines that the effect size in the population of males is
d
= 0.36; Researcher B determines that the effect size in the population of females is
d
= 0.20. All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Two researchers make a test concerning the levels of marital satisfaction among military families. Researcher A collects a sample of 22 married couples (
n
= 22); Researcher B collects a sample of 40 married couples (
n
= 40). All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Two researchers make a test concerning standardized exam performance among senior high school students in one of two local communities. Researcher A tests performance from the population in the northern community, where the standard deviation of test scores is 110 (
); Researcher B tests performance from the population in the southern community, where the standard deviation of test scores is 60 (
). All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Problem Set 3.3: Hypothesis, Direction, and Population Mean
Criterion:
Explain the relationship between hypothesis, tests, and population mean.
Instructions:
Read the following and answer the questions.
T-Test
The document discusses the t-test, which is a statistical method used to determine if there is a significant difference between the means of two groups. It can be used to compare the means of two independent groups, related groups, or a group's mean to a hypothesized population mean. There are assumptions that must be met for a t-test, including independent observations, normal distribution of data, and homogeneity of variances. The t-test calculates a t-score or t-value which is compared to a critical value to determine if the null hypothesis can be rejected.
PAGE O&M Statistics – Inferential Statistics Hypothesis Test.docx
PAGE
O&M Statistics – Inferential Statistics: Hypothesis Testing
Inferential Statistics
Hypothesis testing
Introduction
In this week, we transition from confidence intervals and interval estimates to hypothesis testing, the basis for inferential statistics. Inferential statistics means using a sample to draw a conclusion about an entire population. A test of hypothesis is a procedure to determine whether sample data provide sufficient evidence to support a position about a population. This position or claim is called the alternative or research hypothesis.
“It is a procedure based on sample evidence and probability theory to determine whether the hypothesis is a reasonable statement” (Mason & Lind, pg. 336).
This Week in Relation to the Course
Hypothesis testing is at the heart of research. In this week, we examine and practice a procedure to perform tests of hypotheses comparing a sample mean to a population mean and a test of hypotheses comparing two sample means.
The Five-Step Procedure for Hypothesis Testing (you need to show all 5 steps – these contain the same information you would find in a research paper – allows others to see how you arrived at your conclusion and provides a basis for subsequent research).
Step 1
State the null hypothesis – equating the population parameter to a specification. The null hypothesis is always one of status quo or no difference. We call the null hypothesis H0 (H sub zero). It is the hypothesis that contains an equality.
State the alternate hypothesis – The alternate is represented as H1 or HA (H sub one or H sub A). The alternate hypothesis is the exact opposite of the null hypothesis and represents the conclusion supported if the null is rejected. The alternate will not contain an equal sign of the population parameter.
Most of the time, researchers construct tests of hypothesis with the anticipation that the null hypothesis will be rejected.
Step 2
Select a level of significance (α) which will be used when finding critical value(s).
The level you choose (alpha) indicates how confident we wish to be when making the decision.
For example, a .05 alpha level means that we are 95% sure of the reliability of our findings, but there is still a 5% chance of being wrong (what is called the likelihood of committing a Type 1 error).
The level of significance is set by the individual performing the test. Common significance levels are .01, .05, and .10. It is important to always state what the chosen level of significance is.
Step 3
Identify the test statistic – this is the formula you use given the data in the scenario. Simply put, the test statistic may be a Z statistic, a t statistic, or some other distribution. Selection of the correct test statistic will depend on the nature of the data being tested (sample size, whether the population standard deviation is known, whether the data is known to be normally distributed).
The sampling distribution of the test statistic is divided into t.
This is my statistics exam I need help I have been lost this whole s.docx
This is my statistics exam I need help I have been lost this whole semester
Identify each of the following as examples of nominal, ordinal, interval, or ratio scales of measurement.
1.Identify each of the following as examples of nominal, ordinal, interval, or ratio scales of measurement.
1. A poll of registered voters in Florida asking which candidate they support
2. The length of time required for a wound to heal when using a new medicine
3. The number of telephone calls arriving at a switchboard per five-minute period
4. The distance first-year college football players can kick a ball
5. Mental health diagnoses present in an elderly population
6. The rankings of employees on their job performance
2) Two hundred raffle tickets are sold. Your friend has five people in her family who each bought two raffle tickets. What is the probability that someone from her family will win the raffle?
3) Jolie has 45 minutes to do her statistics homework. If the mean is 38 minutes and the standard deviation is 3, calculate Jolie's z score. Once calculated, interpret your findings in terms of Jolie's performance.
4) . A psychologist measures units of change for a memory test after students are given an opportunity to sleep only four hours. The following change units were obtained: 7, -12, 4, -7, 3, -10. Find the a) mean, b) median, c) mode, d) standard deviation, e) range, and f) variance.
5) A student scored 81 on a chemistry test and 75 on a history test. For the chemistry test, the mean was 70 and the standard deviation was 20. For the history test, the mean was 65 and the standard deviation was 8. Did the student do better on the chemistry test or the history test? Explain your answer
6. Suppose you want to figure out what to do with your degree in psychology. You ask some fellow students from your psychology program who recently graduated to find out what they are doing with their degree and how much it pays. What type of sampling is this? What are the limitations of this sampling approach?
7. Variables in which the values are categories are known as)
Interval variables
Nominal variables
Ordinal variables
Ratio variables
8. Before the researcher can conduct a statistical test, the research question must be translated into
A testable hypothesis
Additional observations
Mathematical symbols
Numbers
9. The hypothesis stating that there are no differences, effects, or relationships is)
The alternative hypothesis
The baseline hypothesis
The null hypothesis
The reasonable hypothesis
10. A group of students made the following scores on a 10-item quiz in psychological statistics: {5, 6, 7, 7, 7, 8, 8, 9, 9, 10, 10} What is the mean score?
6.6
7.2
7.8
8.7
11. A group of students made the following scores on a 10-item quiz in psychological statistics: {5, 6, 7, 7, 7, 8, 8, 9, 9, 10, 10} What is the median score?
6
7
8
9
12. A group of students made the following scores on a 10-item quiz in psychological statistics: {5, 6, 7, 7, 7, 8, 8, 9, 9, 10, 1.
Analyzing experimental research data
This document discusses different statistical tests used to analyze experimental research data, including the t-test, analysis of variance (ANOVA), and chi-square test. It provides examples of how to apply each test and interpret the results. The t-test is used to compare the means of two groups, ANOVA is used for comparing more than two groups, and chi-square is used to analyze relationships between categorical variables. Computer programs like SPSS can perform these statistical analyses to help researchers evaluate experimental data.
The t Test for Two Related Samples
My Slides in Advanced Statistics Summer 2014
If you like it and if you find it useful, just like it. <3 Thanks!
Spss2 comparing means_two_groups
This document discusses different types of t-tests used to compare means: one sample t-tests, independent samples t-tests, and paired samples t-tests. It provides examples and steps for conducting each type of t-test in SPSS. Key points include that one sample t-tests compare a sample mean to a known value, independent samples t-tests compare means between two unrelated groups, and paired samples t-tests compare means within the same group across two time points or conditions. The document also outlines assumptions, how to interpret output and p-values, and how to report results for each t-test. Three cases are presented to demonstrate application of each t-test type.
Ttest
The document provides an overview of student's t-test, a statistical technique used to determine if there are significant differences between the means of two groups. It discusses the different types of t-tests, including one-sample, independent samples, and paired/correlated samples t-tests. Key steps for conducting each type of t-test are outlined, including calculating t-values, degrees of freedom, and comparing results to significance levels. The t-test is a commonly used hypothesis testing tool that allows testing assumptions about population means.
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11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS11 .docx
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS11 .docx
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS11 .docx
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© 2014 Laureate Education, Inc. Page 1 of 5 Week 4 A.docx
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Assessment 3 – Hypothesis, Effect Size, Power, and t Tests.docx
Assessment 3 – Hypothesis, Effect Size, Power, and t Tests.docx
PAGE O&M Statistics – Inferential Statistics Hypothesis Test.docx
PAGE O&M Statistics – Inferential Statistics Hypothesis Test.docx
This is my statistics exam I need help I have been lost this whole s.docx
This is my statistics exam I need help I have been lost this whole s.docx
P1 Stroop
- 1. Stroop Effect P1 Q: What is our independent variable? What is our dependent variable? A: The independent variable is the two forms of the test either color matching word or color mismatching word. The dependent variable is reaction time between the task and response.[1] Q: What is an appropriate set of hypotheses for this task? What kind of statistical test do you expect to perform? Justify your choices. A: Two hypotheses are most likely to occur. Either there will be no too little effect in the test results creating a null hypothesis. However, an alternate hypothesis may show results that color matching word has a lower response time than color mismatching word.[1] Indicating that the incongruent’s reaction time data is significantly higher than the congruent’s reaction time data. • Null Hypothesis H0 = The data between the two results will not differ significantly. In which the t-statistic do not differ from alpha of 0.05. In which Ho : µ1 = µ2 [7] • Alternative Hypothesis H1 = There will be a non-random significant difference between the data of the two results. In which the t-statistic differ from alpha of 0.05. In which H1 : µ1 = µ2 [7] • µ1 is the mean of population 1 and µ2 is the mean of population 2. We can use a two sample T-Test considering the sample size is less than 30 and the standard deviation of the population is unknown.[6] Also both populations are independent for data, but each person has two measurements.[5] Q: Report some descriptive statistics regarding this dataset. Include at least one measure of central tendency and at least one measure of variability. A: Congruent Incongruent Mean 14.05 22.02 Mode N/A N/A Median 14.36 21.02 Variance 12.67 23.01 Std.Deviation 3.56 4.80 IQR 4.31 5.33 Range 13.70 8.84 Q: Provide one or two visualizations that show the distribution of the sample data. Write one or two sentences noting what you observe about the plot or plots. Fig 1: Statistical analysis of Congruent and Incongruent Stroop data.
- 2. . We can see from all three figures that on average the reaction time when performing congruent test is lower than that of the incongruent test for the subjects. From figure 2 with removal of the outlier using the IQR range we can see the congruent curve is positively skewed, while the incongruent curve is more of a normal distribution. This might be indicative that some 0 2 4 6 8 0 4 8 12 16 20 24 28 32 36 40 Frequency Reaction Time (seconds) Congruent Vs. Incongruent Without Ourliers Congruent Incongruent 0 2 4 6 8 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Frequency Reaction Time (s) Congruent Vs. Incongruent Without Ourliers Congruent Incongruent 0.00 5.00 10.00 15.00 20.00 25.00 Congruent Incongruent ReactionTime(S) Average Reaction Time Congruent Vs. Incongruent Fig 2: Congruent Vs Incongruent histogram with outliers.[2] Fig 3: Congruent Vs Incongruent histogram without outliers.[2] Fig 4: Average reaction time of Congruent Vs Incongruent
- 3. individuals are not as greatly affected by the incongruent task as others. While most individuals are able to perform the congruent task with ease. Q: Now, perform the statistical test and report your results. What is your confidence level and your critical statistic value? Do you reject the null hypothesis or fail to reject it? Come to a conclusion in terms of the experiment task. Did the results match up with your expectations? A: Confidence level is 95%, Critical statistic value 0.05[3] Congruent Incongruent Difference Mean 14.05 22.02 -7.96 Variance 12.67 23.01 Observations 24 24 Pearson Correlation 0.35 Hypothesized Mean Difference 0 df 23 t Stat -8.0207 P(T<=t) one-tail 2.052E-08 t Critical one-tail 1.7139 P(T<=t) two-tail 4.103E-08 t Critical two-tail 2.069 Since P-Value two-tail for the T-Test is less than critical statistic value of 0.05. In conclusion I reject the null hypothesis and support the alternative hypothesis.[3] . There is a significant difference between mean times for the task. It does take significantly longer to complete the incongruent task as compared to the congruent task. This is seen by the difference of the means of approximately -7.96 seconds. These results do match my expectations and are in line with the results I received when taking the Stroop test myself. Q: What do you think is responsible for the effects observed? Can you think of an alternative or similar task that would result in a similar effect? Some research about the problem will be helpful for thinking about these two questions! A: This may be due to humans being conditioned to deciphering words in everyday life that our brains automatically focus on figuring out the word and ignore color initially unless forced to. This reduces the reaction time of the person as they have use time confirming the color. You could perform a numerical test where the numbers differ in both numerical and physical size and ask participants which number is numerically larger.[4] References: [1]: Stroop, J.R. Studies of interference in serial verbal reactions. J. Exp. Psychol., 18:643-662, 1935 Fig 4: Two Sample T-Test[3]
- 4. [2]: Create A Histogram. (n.d.). Retrieved January 17, 2017, from https://support.office.com/en-us/article/Create-a-histogram-b6814e9e-5860-4113-ba51- e3a1b9ee1bbe [3]: Chieh, C. J. (n.d.). Making Sense of the Two-Sample T-Test. Retrieved January 17, 2017, from https://www.isixsigma.com/tools-templates/hypothesis-testing/making-sense-two-sample- t-test/ [4]: Henik, A. & Tzelgov, J. Mem Cogn (1982) 10: 389. doi:10.3758/BF03202431 [5]: Types of T-Test. Retrieved January 17, 2017. from http://support.minitab.com/en- us/minitab/17/topic-library/basic-statistics-and-graphs/hypothesis-tests/tests-of-means/types-of- t-tests/ [6]: Andale. T-Score Vs. Z-Score What’s the difference? Published August 23, 2013, Retrieved January 17, 2017. From http://www.statisticshowto.com/when-to-use-a-t-score-vs-z- score/ [7]: What is Hypothesis Testing?. Retrieved January 17, 2017. From http://stattrek.com/hypothesis-test/hypothesis-testing.aspx