The choice of statistical tests depends on the type of data collected and the research question being investigated. Generally, parametric tests are used for normally distributed continuous data, while non-parametric tests are used for non-normally distributed data or categorical data. It is important to consult with a statistician or use online resources to determine the appropriate tests for your specific data.
Descriptive statistics involve summarizing and describing the characteristics of the data, such as measures of central tendency and variability. Inferential statistics, on the other hand, involve making inferences and generalizations about a larger population based on the data collected from a smaller sample. Descriptive statistics are used to understand the data, while inferential statistics are used to draw conclusions and make predictions.
Missing data can be handled in several ways, depending on the reason for the missing values and the type of analysis being conducted. If the missing data is random, it can be excluded from the analysis. However, if the missing data is non-random, imputation techniques can be used to estimate the missing values. It is important to carefully consider the best approach for handling missing data in order to avoid bias in the results.
Some common mistakes to avoid when analyzing data from an experiment include: not clearly defining the research question or hypothesis, not using appropriate statistical tests, not considering the assumptions of the statistical tests, not properly handling outliers or missing data, and not interpreting the results in the context of the research question. It is important to carefully plan and execute the data analysis process in order to ensure accurate and meaningful results.
Data visualization, such as graphs and charts, can be useful in understanding and communicating the results of an experiment. It can help identify patterns, trends, and relationships in the data that may not be apparent from numerical summaries alone. Data visualization can also be used to effectively present the results to others, making it a valuable tool in the data analysis process.