In scientific publications, p-hacking, small sample sizes, and erroneous correlations are well known. When under pressure to publish significant study findings, these are simple mistakes to make. Significant outcomes are considerably easier to publicize than unsuccessful studies. Although it’s a regrettable statement, it’s the reality.

The majority of purported research findings can be demonstrated to be untrue. There are several issues with his proof; the majority of his detractors concur with that research article. In fact, it’s not uncommon for us to read in the news about research study findings. It seems to contradict findings from studies that were published only a few years prior.

Lack of knowledge about statistical techniques, their appropriate application, and their limits is the main cause of the issue. This post will discuss typical errors in statistics and provide advice on how to prevent them.

**Importance of Statistics**

Prior to delving into the typical errors, let us acknowledge the importance of statistics. It supports our ability to measure uncertainty, identify trends, and verify theories.

Utilizing statistics to support and convince your audience is a great idea. Since statistics are frequently taken out of context, they can be deceptive. Occasionally, in an attempt to heighten the dramatic effect of the statistic, essential details regarding its collection are omitted.

When a critical value calculator is used properly, statistics can strengthen your case. They can give your work more credibility by demonstrating that there is proof to support your claims. Your audience will frequently react emotionally when they see statistics.

The use of statistics is essential to research. It enables us to make predictions, draw valid conclusions, and guide evidence-based decision-making.

**Common Mistakes in Statistics**

**Expecting Too Much Certainty**

There is uncertainty everywhere we look; we cannot expect clarity. However, uncertainty can be “quantified”; that is, we can discuss different levels of assurance or uncertainty. This is how probability works. A greater probability indicates a greater level of confidence that an event will occur.

The purpose of statistical approaches is to assist us in comprehending situations where uncertainty exists and can be measured. The majority of statistical methods rely on probability.

Statistics is the study of optimal strategies for handling randomness. People tend to appreciate having information but dislike uncertainty, especially when they need to make judgments. But in reality, uncertainty and information are only two sides of the same coin.

**Misunderstandings About Probability**

Classical, empirical, irrational, and axiomatic are the four most common approaches to probability. If one viewpoint is used while another is intended, mistakes and misunderstandings might result.

The first formal introduction to probability for pupils is something that many authors have noted. Whether in middle school or high school is frequently from the traditional viewpoint. However, many applications of probability involve circumstances in which there may be unequal odds for certain events.

**Errors in Sampling**

The term “random sample” is commonly used incorrectly. First of all, the definition of the word “random” in the phrase “random sample” is not what most people would consider to be typical. It doesn’t allude to the definition that would appear first in a dictionary.

Applying this conventionally leads to the typical error of believing that a sample is not random because it exhibits a pattern. In fact, a random sample may or may not show a trend. In actuality, there is no way to know if the sample is a random sample just by looking at it.

**Problematic Choices of a Measure**

Measurement variance limits the use of even the most basic statistical techniques.

One or more outcome variables are typically measured in research. The outcome measures are analyzed, and conclusions are formed from the statistical analysis. Care must be given when phrasing questions because outcome measurements might occasionally be questions themselves.

Another option for a measure is frequently referred to as a summary statistic. It is frequently used in the statistical analysis itself. Undervaluing the selection of outcome variables or summary statistics is a typical cause of inaccurate research findings. Making a wise decision is dependent on the specifics of the situation, especially the research question.

**Using an Inappropriate Method of Analysis**

The reliability of the underlying assumptions places restrictions on all models. Model assumptions are rarely expressed or even defended. The issue is made worse by publications’ proclivity for minor novelty in statistical techniques.

Model assumptions underlie each frequentist inference method. Model assumptions vary between different methodologies. Whether or not the model assumptions are true in the context of the data being examined determines whether or not the technique is valid.

Many methods can withstand changes to at least some model presumptions. This indicates that the method is still roughly valid if the specific assumption is not too far from being true.

**How to Avoid Common Mistakes**

**Comprehend the Data**

Get to know your data thoroughly to start. This includes determining whether any values are missing, comprehending the variables, and visualizing the data. The cornerstone of a precise analysis is a firm understanding of your facts.

**Select the Appropriate Statistical Tests**

It is crucial to choose the right statistical test. Make sure you are aware of the data you have and the hypothesis you are testing. In complicated situations, consulting a statistician may be required.

**Eliminate sampling biases**

Use random sample strategies to lessen sampling bias. Consider stratified sampling to make sure every grouping is sufficiently represented if certain groups are underrepresented.

**Handle Outliers Correctly**

Results may be skewed by outliers. Ascertain whether these are accurate data points or mistakes. Consider using robust statistical techniques that are less susceptible to outliers if they are valid.

**Final Verdict**

Statistics are a useful tool in decision-making, but they can also be ineffective if used incorrectly.

The best statistical tests should be chosen, sampling bias should be addressed, and outliers should be handled correctly in order to prevent these mistakes.

In order to cut down on errors, technology is also essential. These recommendations will enable you to fully utilize statistics and base your judgments on reliable information.

## Other articles

Please read through some of our other articles with examples and explanations if you’d like to learn more.

**Statistics**

- PLS-SEM model
- Principal Components Analysis
- Multivariate Analysis
- Friedman Test
- Chi-Square Test (Χ²)
- T-test
- SPSS
- Effect Size
- Critical Values in Statistics
- Statistical Analysis
- Calculate the Sample Size for Randomized Controlled Trials
- Covariate in Statistics
- Avoid Common Mistakes in Statistics
- Standard Deviation
- Derivatives & Formulas
- Build a PLS-SEM model using AMOS
- Principal Components Analysis using SPSS
- Statistical Tools
- Type I vs Type II error
- Descriptive and Inferential Statistics
- Microsoft Excel and SPSS
- One-tailed and Two-tailed Test
- Parametric and Non-Parametric Test

**Citation Styles**

**Comparision**

- Independent vs. Dependent Variable – MIM Learnovate
- Research Article and Research Paper
- Proposition and Hypothesis
- Principal Component Analysis and Partial Least Squares
- Academic Research vs Industry Research
- Clinical Research vs Lab Research
- Research Lab and Hospital Lab
- Thesis Statement and Research Question
- Quantitative Researchers vs. Quantitative Traders
- Premise, Hypothesis and Supposition
- Survey Vs Experiment
- Hypothesis and Theory
- Independent vs. Dependent Variable
- APA vs. MLA
- Ghost Authorship vs. Gift Authorship
- Basic and Applied Research
- Cross-Sectional vs Longitudinal Studies
- Survey vs Questionnaire
- Open Ended vs Closed Ended Questions
- Experimental and Non-Experimental Research
- Inductive vs Deductive Approach
- Null and Alternative Hypothesis
- Reliability vs Validity
- Population vs Sample
- Conceptual Framework and Theoretical Framework
- Bibliography and Reference
- Stratified vs Cluster Sampling
- Sampling Error vs Sampling Bias
- Internal Validity vs External Validity
- Full-Scale, Laboratory-Scale and Pilot-Scale Studies
- Plagiarism and Paraphrasing
- Research Methodology Vs. Research Method
- Mediator and Moderator
- Type I vs Type II error
- Descriptive and Inferential Statistics
- Microsoft Excel and SPSS
- Parametric and Non-Parametric Test

** Research **

- Table of Contents
- Dissertation Topic
- Synopsis
- Thesis Statement
- Research Proposal
- Research Questions
- Research Problem
- Research Gap
- Types of Research Gaps
- Variables
- Operationalization of Variables
- Literature Review
- Research Hypothesis
- Questionnaire
- Abstract
- Validity
- Reliability
- Measurement of Scale
- Sampling Techniques
- Acknowledgements
- Research Methods
- Quantitative Research
- Qualitative Research
- Case Study Research
- Survey Research
- Conclusive Research
- Descriptive Research
- Cross-Sectional Research
- Theoretical Framework
- Conceptual Framework
- Triangulation
- Grounded Theory
- Quasi-Experimental Design
- Mixed Method
- Correlational Research
- Randomized Controlled Trial
- Stratified Sampling
- Ethnography
- Ghost Authorship
- Secondary Data Collection
- Primary Data Collection
- Ex-Post-Facto