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Last updated on 2024年5月27日

How can you use hypothesis testing to validate your experiment design?

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If you are applying lean manufacturing principles to your processes, you know that running experiments is a key way to test and improve your performance. But how can you be sure that your experiment design is valid and reliable? How can you avoid wasting time and resources on inconclusive or misleading results? One answer is to use hypothesis testing, a statistical method that helps you evaluate your assumptions and compare your outcomes. In this article, you will learn how to use hypothesis testing to validate your experiment design and make better decisions.

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Ujjal Mishra

"24 years of Senior Quality Assurance Expert in Quality Management/Lean Manufacturing/Customer Experience/Manufacturing…
Marcello Soares

CEO & Founder - Labone Consultoria e Treinamentos | Lean Six Sigma Master Black Belt | Trainings and Consulting | Top…

1 What is hypothesis testing?

Hypothesis testing is a way of testing whether a claim or a prediction about a population parameter (such as a mean, a proportion, or a difference) is supported by the data. A hypothesis is a statement that can be either true or false, and that you want to verify or reject based on evidence. For example, you might have a hypothesis that a new machine reduces the defect rate by 10%. To test this hypothesis, you need to collect and analyze data from both the old and the new machine, and compare them using a statistical test.

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Ujjal Mishra

"24 years of Senior Quality Assurance Expert in Quality Management/Lean Manufacturing/Customer Experience/Manufacturing Operations/Communication/Certified Lean Six Sigma Yellow, Green & Black Belt.
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Hypothesis testing is a statistical method used to evaluate whether a claim or hypothesis about a population parameter (e.g., mean, proportion) should be accepted or rejected based on sample data. It involves formulating a null hypothesis and an alternative hypothesis and using statistical evidence to determine which hypothesis is supported.

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Marcello Soares

CEO & Founder - Labone Consultoria e Treinamentos | Lean Six Sigma Master Black Belt | Trainings and Consulting | Top Lean Manufacturing & Lean Six Sigma Voice | Leadership Development Expert
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Primeiramente, estabele?o uma hipótese nula (H0), que representa a condi??o atual, e uma hipótese alternativa (H1), que reflete a mudan?a desejada. Em seguida, seleciono uma amostra representativa e aplico técnicas estatísticas, como o teste t de Student ou ANOVA, dependendo do número de grupos envolvidos. Para validar o desenho do experimento, realizo o teste de significancia, onde comparo os resultados observados com um nível de significancia predefinido (geralmente 0,05). Se os resultados estiverem além desse limite, rejeito a hipótese nula e considero o desenho do experimento válido. Além disso, uso gráficos de controle para monitorar continuamente o processo, garantindo a estabilidade e confiabilidade.

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Thiago Loretti

Gerente de Qualidade, Sustentabilidade e Processos | Professor Lato Sensu na Funda??o Assis Gurgacz | Pesquisador de Kaizen | Master Black Belt em Lean Six Sigma | Auditor Líder ISO 9001 e 14001
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Para validar um design experimental usando testes de hipóteses, primeiro, defino claramente a hipótese nula (H0) e a hipótese alternativa (H1) relacionadas ao experimento. Em seguida, escolho um teste estatístico apropriado com base na natureza dos dados e das hipóteses. Coletando dados conforme o design experimental, aplico o teste estatístico selecionado para calcular o valor-p ou o intervalo de confian?a. Se o valor-p for menor que um nível de significancia pré-definido, rejeito a hipótese nula e aceito a hipótese alternativa, confirmando a eficácia do design experimental. Caso contrário, n?o posso rejeitar a hipótese nula, indicando que n?o há evidências suficientes para suportar o design proposto.

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2 How to set up a hypothesis test?

Before you run a hypothesis test, you need to define two hypotheses: the null hypothesis and the alternative hypothesis. The null hypothesis is the default assumption that there is no difference or no effect of the factor you are testing. The alternative hypothesis is the opposite of the null hypothesis, and it represents the difference or the effect that you expect or want to detect. For example, if you are testing whether a new machine reduces the defect rate by 10%, your null hypothesis would be that the defect rate is the same for both machines, and your alternative hypothesis would be that the defect rate is 10% lower for the new machine.

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Ujjal Mishra

"24 years of Senior Quality Assurance Expert in Quality Management/Lean Manufacturing/Customer Experience/Manufacturing Operations/Communication/Certified Lean Six Sigma Yellow, Green & Black Belt.
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Setting up a hypothesis test involves defining the null hypothesis (H0) and the alternative hypothesis (H1). The null hypothesis typically represents the status quo or the claim being tested, while the alternative hypothesis is the opposite of what the null hypothesis states. The hypotheses must be mutually exclusive and collectively exhaustive.

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3 How to choose a significance level?

The significance level, also called alpha, is the probability of rejecting the null hypothesis when it is true. In other words, it is the risk of making a false positive error, or concluding that there is a difference or an effect when there is none. The significance level is usually set at 0.05, which means that you are willing to accept a 5% chance of making a false positive error. However, you can choose a different significance level depending on your context and your goals. For example, if you are testing a critical safety feature, you might want to use a lower significance level, such as 0.01, to reduce the risk of making a false positive error.

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Ujjal Mishra

"24 years of Senior Quality Assurance Expert in Quality Management/Lean Manufacturing/Customer Experience/Manufacturing Operations/Communication/Certified Lean Six Sigma Yellow, Green & Black Belt.
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The significance level (α) is the probability of rejecting the null hypothesis when it is true. It is set before the test and represents the level of risk the researcher is willing to take for committing a Type I error (falsely rejecting a true null hypothesis). Common significance levels are 0.05 (5%) or 0.01 (1%).

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4 How to calculate a test statistic and a p-value?

The test statistic is a numerical value that measures how far your sample data is from the null hypothesis. The test statistic depends on the type of data and the type of test you are using. For example, if you are testing the difference between two means, you might use a t-test or a z-test, and calculate a t-value or a z-value as your test statistic. The p-value is the probability of obtaining a test statistic as extreme or more extreme than the one you observed, assuming that the null hypothesis is true. The p-value tells you how likely it is that your data is compatible with the null hypothesis. For example, if you get a p-value of 0.02, it means that there is a 2% chance of observing a test statistic as extreme or more extreme than yours, if the null hypothesis is true.

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Ujjal Mishra

"24 years of Senior Quality Assurance Expert in Quality Management/Lean Manufacturing/Customer Experience/Manufacturing Operations/Communication/Certified Lean Six Sigma Yellow, Green & Black Belt.
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The test statistic is a value calculated from the sample data using a formula specific to the hypothesis test being conducted (e.g., t-test, z-test, ANOVA). The p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming the null hypothesis is true. Smaller p-values provide stronger evidence against the null hypothesis.

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5 How to make a decision based on the p-value?

The final step of hypothesis testing is to compare the p-value with the significance level, and make a decision to either reject or fail to reject the null hypothesis. If the p-value is less than or equal to the significance level, you reject the null hypothesis, and conclude that there is enough evidence to support the alternative hypothesis. If the p-value is greater than the significance level, you fail to reject the null hypothesis, and conclude that there is not enough evidence to support the alternative hypothesis. For example, if you get a p-value of 0.02, and your significance level is 0.05, you reject the null hypothesis, and accept that the new machine reduces the defect rate by 10%.

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6 How to interpret and communicate your results?

After you make a decision based on the p-value, you need to interpret and communicate your results in a clear and concise way. You should avoid using terms like "prove" or "confirm", as hypothesis testing does not provide absolute certainty, but rather a degree of confidence. You should also report the effect size, which is a measure of how large or how meaningful the difference or the effect is. For example, you might say: "Based on a t-test, we reject the null hypothesis that the defect rate is the same for both machines, and conclude that the new machine reduces the defect rate by 10%, with a p-value of 0.02 and a 95% confidence interval of (7%, 13%). The effect size is 0.5, which indicates a medium effect."

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you use hypothesis testing to validate your experiment design?

1

2

3

4

5

6

7

1 What is hypothesis testing?

2 How to set up a hypothesis test?

3 How to choose a significance level?

4 How to calculate a test statistic and a p-value?

5 How to make a decision based on the p-value?

6 How to interpret and communicate your results?

7 Here’s what else to consider

Lean Manufacturing

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How can you use hypothesis testing to validate your experiment design?

1

2

3

4

5

6

7

1 What is hypothesis testing?

2 How to set up a hypothesis test?

3 How to choose a significance level?

4 How to calculate a test statistic and a p-value?

5 How to make a decision based on the p-value?

6 How to interpret and communicate your results?

7 Here’s what else to consider

Lean Manufacturing

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