Definitions

Question 1

Histogram

Answer 1

A histogram is a graph that presents continuous numerical data in bars. The height of a rectangle (the vertical axis) represents the distribution frequency of a variable (the amount, or how often that variable appears).

Question 2

T-test

Answer 2

T-tests are to test whether the mean differences between groups are different. An independent samples t-test is used to assess the mean differences between two independent tests.

Question 3

Degrees of freedom

Answer 3

Degrees of freedom show us how much the data can vary before it effects our results.

Question 4

sampling distribution of mean differences

Answer 4

The sampling distribution of mean differences refers to the distribution of differences in means obtained from multiple samples taken from the same population

Question 5

p-value

Answer 5

the probability of observing a difference in sample means as extreme as (or more extreme than) the one obtained in the current study, assuming the null hypothesis is true.

Question 6

relative frequency

Answer 6

Relative frequency is the proportion or percentage of occurrences of a specific category or data point relative to the total number of occurrences in the dataset.
It provides a way to understand the distribution of data in terms of proportions rather than absolute counts.

Question 7

cumulative frequency

Answer 7

Cumulative frequency is the frequency of all of the occurrences up till the one you have chosen. This is calculated as the sum of all the frequencies up until the chosen point.

Question 8

Standard Deviations

Answer 8

Standard deviation is a statistical measure of the dispersion or spread of a set of data points. It quantifies how much individual data points differ from the mean (average) of the data set. In other words, it indicates the average deviation of each data point from the mean.

Question 9

Statistical inference

Answer 9

Statistical inference is the process of making predictions, decisions, or generalizations about a population based on sample data from that population. It involves using statistical techniques to draw conclusions about parameters or characteristics of a population by analyzing sample data.

Question 10

standard error of the mean

Answer 10

The standard error of the mean is a measure of the variability or dispersion of sample means around the population mean.
It indicates how much the sample mean is expected to vary from one sample to another.

Question 11

z-scores

Answer 11

They represent the number of standard deviations a data point is from the mean of the distribution. This standardization process allows for the comparison of data points from different distributions or with different units of measurement.

Z-scores are used to standardise data points so that the mean is 0 and the standard deviation is 1.

Question 12

Pearson Bivariate Correlation Coefficient

and range

Answer 12

used to assess the strength and the direction of the relationship between two variables, IV and DV.
The range is between -1 and 1.

Question 13

R-squared

Answer 13

indicates the proportion of the variance in the dependent variable (outcome) that is explained by the independent variable(s) (predictor(s)).
It measures the goodness of fit of the regression model to the observed data.
R-squared ranges from 0 to 1.

Question 14

R-squared value indicate in a regression model

Answer 14

When there is only one independent variable in a simple linear regression model, the correlation coefficient between the independent variable and the dependent variable is equivalent to the square root of the R-squared value.

Question 15

confidence interval for a correlation coefficient

Answer 15

confidence interval for a correlation coefficient involves assessing the range within which we can reasonably estimate the true value of the correlation in the population.

Question 16

The Intercept

Answer 16

the value of Y (DV) when X (IV) is 0.

Question 17

Regression Coefficients

Answer 17

parameters that represent the strength and direction of the relationship between two variables.
in a simple regression model, this represents the change in the dependent variable given one unit of change in the independent variable.

Question 18

The residuals

Answer 18

defined as the difference between the predicted values and the observed values.

Question 19

Error term

Answer 19

refers to the changes in the DV that cannot be explained by the changes in the IV. This can be naturally occurring.

Question 20

power

Answer 20

Statistical power in the context of hypothesis testing refers to the probability of correctly rejecting a null hypothesis when it is false. In other words, it represents the ability of a statistical test to detect a true effect or difference if one exists.

Question 21

Cohen’s d

Answer 21

Cohen’s d is a measure of effect size used to quantify the difference between two groups. It helps determine how large or small the difference is between the means of the groups
a small effect size is 0.2, a medium effect size is 0.5 and a large size is 0.8.

Question 22

effect size

Answer 22

the magnitude of the effect or difference that the study aims to detect.

Question 23

Regression

Answer 23

a statistical technique used to model the relationship between one or more predictor variables and a response variable. It aims to understand how changes in the predictor variables are associated with changes in the response variable.