SPSS Important Tests and Interpretation- A Practical Guide | Free Udemy Course

Population: A population is a complete group of individuals or objects that share a common characteristic, such as all people in a specific country or all plants in a particular region. In statistical analysis, the population represents the larger group that the researcher is interested in studying.

Sample: A sample is a smaller group of individuals or objects selected from a population. The sample is used to represent the larger population and to make inferences about the population based on the characteristics of the sample. Sampling is a crucial aspect of statistical analysis, as it allows researchers to study a portion of the population, rather than the entire population, which can be time-consuming and resource-intensive.

Inferential Statistics: Inferential statistics is a branch of statistics that uses sample data to make inferences about a population. The goal of inferential statistics is to use sample data to make generalizations about the population from which the sample was drawn. Inferential statistics involves a variety of statistical methods and models that allow researchers to estimate population parameters, test hypotheses, and make predictions about the population based on sample data. This type of statistics is often used to test theories, make decisions, and draw conclusions based on sample data, rather than on the entire population.

The mode and median are two different measures of central tendency that are used to describe the center of a data set. The difference between mode and median is:

1. Mode: The mode is the most frequently occurring value in a data set. If a data set has multiple values that occur with equal frequency, then it is said to have multiple modes.

2. Median: The median is the middle value of a data set when the values are arranged in order. If the data set has an odd number of values, the median is the middle value. If the data set has an even number of values, the median is the average of the two middle values.

Central tendency refers to a single value or summary statistic that represents the "center" of a set of data. The central tendency helps to describe the typical or average value of a data set and is used to represent the entire data set in a single number. The most commonly used measures of central tendency are mean, median, and mode.

The p-value is a statistical measure used in hypothesis testing to determine the significance of an observed result. The p-value represents the probability of obtaining an observed result, assuming that the null hypothesis is true. The null hypothesis is a statement that there is no difference between the observed and expected results.

If the p-value is less than a predetermined significance level (usually 0.05), it is concluded that the observed result is statistically significant and that the null hypothesis can be rejected. This means that the observed result is unlikely to have occurred by chance and suggests a real relationship between the variables being studied.

Probability is a measure of the likelihood of an event occurring. It is expressed as a value between 0 and 1, where 0 indicates that an event is impossible, and 1 indicates that an event is certain to occur.

For example, if the probability of getting heads when flipping a fair coin is 0.5, it means that there is a 50% chance of getting heads and a 50% chance of getting tails.

Probability can be calculated using various methods such as classical probability, empirical probability, and subjective probability. In classical probability, the probability of an event is calculated as the ratio of the number of favorable outcomes to the total number of possible outcomes. In empirical probability, the probability of an event is calculated based on the actual observations made from a sample of the population. In subjective probability, the probability of an event is assigned based on the personal beliefs or opinions of the person.

Type-1 error is the incorrect rejection of a true null hypothesis. It is also known as a false positive. Type-1 error is represented by alpha (α) and is usually set at 0.05, meaning there is a 5% chance of making a Type-1 error.

Type-2 error is the failure to reject a false null hypothesis. It is also known as a false negative. Type-2 error is represented by beta (β) and is inversely related to the power of a test, meaning the higher the power, the lower the chance of making a Type-2 error.

ANOVA (Analysis of Variance) is a statistical method used to compare the means of two or more groups. It tests the null hypothesis that the means of all groups are equal against the alternative hypothesis that at least one mean is different from the others. ANOVA is used to determine if there is a significant difference between the groups, and to identify which groups are significantly different from each other. It can be used for comparing means of continuous data and is appropriate for one-way and multi-way designs.

ANOVA (Analysis of Variance) is a statistical method used to compare the means of two or more groups. It tests the null hypothesis that the means of all groups are equal against the alternative hypothesis that at least one mean is different from the others. ANOVA is used to determine if there is a significant difference between the groups, and to identify which groups are significantly different from each other. It can be used for comparing means of continuous data and is appropriate for one-way and multi-way designs.

Correlation is a statistical relationship between two variables, where one variable tends to change with the other. It measures the strength of the linear relationship between two variables and ranges from -1 to 1. A positive correlation indicates that when one variable increases, the other variable also increases; while a negative correlation indicates that when one variable increases, the other variable decreases. Correlation does not imply causation, as other factors could be responsible for the relationship between the variables. Correlation is useful in identifying trends and patterns in data.

Regression is a statistical method that is used to model the relationship between a dependent variable and one or more independent variables. It helps to analyze the influence of one or more independent variables on the dependent variable. The goal of regression analysis is to find the best-fitting line (or equation) that represents the relationship between the dependent and independent variables. The regression line can be used to make predictions about the value of the dependent variable based on the values of the independent variables. There are several types of regression, including simple linear regression, multiple linear regression, logistic regression, and others.